LangChain 101: A DAOGEN Chronicles Special

Discover the power of LangChain through easy-to-understand examples.

🌌 Embarking on the LangChain Odyssey: An Introduction 🚀

Welcome, fellow creators and adventurers, to another thrilling chapter of the DAOGEN Chronicles! As we continue our extraordinary journey in the world of DAOGEN, it's time to dive deeper into the powerful language orchestration engine that powers our adventures: LangChain.

From crafting engaging dialogues to generating creative narratives, LangChain is the secret behind the magic. It's what enables us to bring our DAOGEN characters to life, creating immersive experiences that captivate and inspire.

In this blog post, we're going to explore LangChain in a way you've never seen before. We'll break down the vast capabilities of this remarkable engine into bite-sized, easy-to-understand examples. Each example will shed light on a different aspect of LangChain, showing you the immense potential it holds.

Whether you're a seasoned developer or a curious newcomer, this post is designed to make LangChain accessible and engaging. We'll guide you through each example, providing clear explanations and demonstrating practical applications.

As you explore these examples, you'll gain a deeper understanding of how LangChain works. You'll discover how it integrates with DAOGEN, how it contributes to the creation of our characters, and how it enhances the overall storytelling experience.

By the end of this post, you'll be well-equipped to harness the power of LangChain in your own projects. You'll be able to create immersive, interactive narratives that push the boundaries of imagination.

So, are you ready to embark on this journey? Let's jump right in and start exploring the fascinating world of LangChain!

#LangChain #LanguageOrchestration #DAOGENChronicles #ImmersiveStorytelling

🙏 Acknowledgements 🙏

Before we delve into the fascinating world of LangChain, I'd like to take a moment to express my gratitude for the incredible contributions that have made our journey in DAOGEN possible.

Firstly, a special shout-out to Mostafa Ibrahim, whose invaluable guidance on connecting LangChain chat to the Slack API has been instrumental in our work. Thank you, Mostafa, for sharing your expertise with us.

We are also indebted to the Activeloop team for their comprehensive course on LangChain and Vector Databases in Production. The insights and foundational knowledge they have provided have truly enriched our work with LangChain.

Our heartfelt appreciation extends to DigitalOcean for their exceptional tutorials on setting up containers and Kubernetes, which have been crucial in ensuring a smooth deployment and scalability of our projects.

Lastly, we would like to acknowledge ChatGPT 4 for its indispensable support throughout the process. Its advanced capabilities and debugging assistance have been invaluable in our journey.

To all of you - Mostafa Ibrahim, the Activeloop team, DigitalOcean, and ChatGPT 4 - thank you for your dedication to advancing the field of AI and enriching the DAOGEN universe. Your efforts have not only enriched our work, but they are also shaping the future of AI agents and immersive storytelling.

#Acknowledgements #LangChain #DAOGENChronicles

🎶 Coding with the Beat: Getting Started with LangChain Examples 🎧

Hello, fellow adventurers! Dj Squircle spinning the decks here, and it's time to dive into the extraordinary world of code. We're going to take a close look at LangChain through practical examples, turning up the volume on our adventure into the technical depths of DAOGEN. Are you ready? Let's get this party started!

First things first, you need to know where the party's happening. All our examples are ready and waiting for you in the GitHub repo. So, get yourself over to our LangChain_Examples repository and start grooving with the code.

Now, let's talk about setting the stage. Before we start dropping any beats, we need to make sure our environment is ready to handle the rhythm. This is where Python virtual environments come into play. They isolate your Python setup on a project-by-project basis, which means no nasty surprises with different projects demanding different versions of packages. Think of it as soundproofing your studio – you wouldn't want your techno beats to clash with your classical compositions, right?

To set up a Python virtual environment, just follow the instructions in our GitHub repository's README file. It's as easy as one, two, three!

Next on our checklist is the OPEN_AI_KEY. This key is your ticket to the LangChain experience. You'll need to obtain your own OPEN_AI_KEY and store it in your local .env file for the example code to work like a charm.

With the environment set and the key in hand, we're almost ready to unleash the power of LangChain. But, like any good DJ, we need to make sure we're equipped with the right gear. Your next task is to install the required dependencies, and the details for this are also in the README file. Just follow along, and you'll be ready to roll in no time!

So, are you ready to take LangChain for a spin? Then let's hit the dancefloor and start coding!

#LangChain #GettingStarted #DJ_Squircle #DAOGENChronicles"

🚀 Surfing the AI Waves: Your First LangChain Code Breakdown 🌊

🚀🎧 Hey there, cosmic coders! This is Dj Squircle, and we're about to embark on an exhilarating journey of code exploration. The code we're about to dissect will use the power of LangChain and OpenAI to generate AI responses to our burning questions. Ready? Let's hit the play button and get this party started! 🎚️💻

python
# Load environment variables from .env file
from dotenv import load_dotenv
load_dotenv()

# Import necessary classes from LangChain
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.llms import OpenAI

# Define the prompt template for our question and answer
prompt = PromptTemplate(template="Question: {question}\nAnswer:", input_variables=["question"])

# Initialize the large language model (LLM) using OpenAI's "text-davinci-003" model with a temperature setting of 0.9
llm = OpenAI(model_name="text-davinci-003", temperature=.9)

# Create a LangChain using the defined LLM and prompt
chain = LLMChain(llm=llm, prompt=prompt)

# Run the chain with a question and print out the output
output = chain.run("What is the meaning of life for a surfer?")

print(output)

This cosmic code is quite the masterpiece, isn't it? Let's take a closer look and decipher its groovy beats:

1. Environment Variables: We start off by loading environment variables from a .env file. This allows us to keep sensitive data (like API keys) out of our code and in a secure place. It's like the secret backstage pass to our show!

python
from dotenv import load_dotenv
load_dotenv()

2. Prompt Templates: Next, we import a `PromptTemplate` from LangChain. This is where we define the structure of our AI conversation. In this case, our template is a simple question and answer format, like a cosmic interview! 🎤

python
from langchain.prompts import PromptTemplate
prompt = PromptTemplate(template="Question: {question}\nAnswer:", input_variables=["question"])

3. Large Language Models: We then import `OpenAI` from LangChain and initialize it with the "text-davinci-003" model. The `temperature` parameter is set to 0.9, which influences the randomness of the AI's output. It's like adjusting the vibe of our DJ set! 🎧

python
from langchain.llms import OpenAI
llm = OpenAI(model_name="text-davinci-003", temperature=.9)

4. LangChain: With our LLM and prompt ready, we can finally construct our LangChain. This is the magic tool that combines our LLM and prompt into a dynamic conversation engine. It's like our AI turntable, ready to spin some incredible responses! 🎚️

python
from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)

5. Running the Chain: Now for the grand finale! We run our LangChain with a question: "What is the meaning of life for a surfer?". The output is then printed to the console, and voilà! We've got our AI's response to the deep question. 🏄‍♂️💬

python
output = chain.run("What is the meaning of life for a surfer?")
print(output)

There you have it, my coding cosmonauts! That's our jam-packed overview of this piece of code, designed to give us AI-powered insights into the philosophical quandaries of surfers. Remember, this is just the first step on a longer journey. So, grab your cosmic surfboard, and let's ride the wave of AI-powered language processing together! 🌌🏄‍♀️🚀

Outro and Call to Action 🚀🎉

Dear fellow DAOGEN adventurers, while we've reached the end of this particular blog post, let it be known that this is not the end of your journey! No, indeed, your adventure is just beginning, and there are many more exciting explorations ahead! 🚀✨

I strongly encourage you to delve into the numerous other examples waiting for you in our GitHub repository. Each one is a treasure trove of knowledge, ready to enhance your skills and broaden your understanding of LangChain and Python.

01.00_surferbro_prompt_template.py
01.01_simple_prompt_template.py
01.02_simple_prompt_template.py
01.03_simple_naming_assistant.py
01.04_simple_summarizer.py
01.05_simple_prompt_template.py
02.01_simple_movie_assistant.py
02.02_simple_movie_assistant.py
03.01_dj_squircle_life_coach_with_few_shot_example_step_by_step.py
03.02_dj_squircle_life_coach_with_few_shot_examples.py
04.01_save_few_shot_example_prompts.py
04.02_load_few_shot_example_prompts.py
04.03_save_several_few_shot_example_prompts.py
04.04_load_several_few_shot_examples.py
05.01_few_shot_example_prompt.py
06.01_how_many_tokens_whats_it_cost.py
07.01_output_parser_csv.py
07.02_output_parser_structured_with_validation.py
08.01_language_translate_and_summarize.py
09.01_prompt_chaining.py
10.01_pdf_summarizing.py
11.01_web_article_summarizer.py
y_web_character_summarizer.py
z_DAOGEN_characters.py

The best part? All of these examples can be run by simply copy-pasting them into the working example we've explored together in this blog post. Yes, you heard that right, fellow explorers! The code environment we've created here acts as a universal key, unlocking every other example in our repository. 🗝️🔓

Each of these additional examples comes with detailed explanations in the form of code comments. So, even if you're just reading the comments, you're still on a path of discovery and learning. We believe that every bit of knowledge, no matter how small, contributes to the larger journey of mastering LangChain and Python.

Don't forget to star and watch our GitHub repository, so you can be the first to know when we add new samples. Your feedback is valuable to us and helps shape the future of our work. If you find the examples useful or have suggestions for improvements, we'd love to hear from you. 🌟👀

Moreover, if you're excited about what we're doing and want to contribute, we'd be thrilled to welcome you to our DAOGEN community. Join us in building new characters and AI agents, and let's redefine the boundaries of AI technology together. There's nothing like being a part of the team that's shaping the future of AI and immersive storytelling. 👥💡

So, my fellow explorers, your adventure continues. Dive into the examples, immerse yourself in the comments, and harness the power of learning through doing. Let's keep pushing the boundaries of what's possible in the DAOGEN universe! 🌌🌠

Until our paths cross again in another adventure, keep exploring, keep learning, and keep the spirit of discovery alive! 🎧🎉

#DAOGENChronicles #LangChainAdventures #PythonJourneys #OpenAI #CommunityLearning