The Hitchhiker’s Guide to LangChain, DeepLake, and OpenAI

As the third sun of the peculiarly named planet Gnarlfrozzle XVIII began to dip below the horizon, Zaphod Breeblebotz, the eccentric and somewhat bewildered software engineer, found himself standing in front of a particularly interesting codebase. He scratched his third head, the one he’d specifically grown for this sort of situation, and gazed at the code with a mix of trepidation and the faintest hint of lemon.

Zaphod had been tasked with unraveling the mysteries of LangChain, GPT, and Deep Lake, and using these technologies to analyse the code base of the LangChain project itself. He was, however, distracted by the fact that his favourite Pan-Galactic Gargle Blaster was still half-full, and he was contemplating whether to take another sip before diving into the code.

In an effort to make the task seem less daunting, he began by separating the LangChain project’s python files into what he called “documents,” a term that he felt was suitably impressive. “Ah yes,” he thought, “the documents. Much better than simply calling them ‘files’.” After splitting the documents into manageable chunks, he proceeded to embed them into the mysterious and powerful Deep Lake.

As he neared completion of the task, Zaphod decided it was time for a little experimentation. He formulated a series of questions to which he thought the code should have answers, and then set about constructing a Conversational Chain to assist in his quest for knowledge.

After the requisite amount of head-scratching, thumb-twiddling, and occasional bouts of existential doubt, Zaphod finally initiated the question-answering portion of his plan. He asked the Conversational Chain about the class hierarchy, the function of certain classes, and the state of unit tests in the ./langchain/utilities/ folder.

The answers came back in a flurry of text, providing Zaphod with the information he sought. He was particularly intrigued by the suggestion to improve the class hierarchy for the Chain class, and made a mental note to look into it further, just as soon as he finished his Pan-Galactic Gargle Blaster.

And so, armed with the knowledge provided by LangChain, GPT, and Deep Lake, Zaphod Breeblebotz ventured forth into the tangled web of code, determined to make sense of it all, or at least make it slightly less nonsensical. And as the light from the third sun finally disappeared from the sky, Zaphod knew that he was, without a doubt, embarking on one of the strangest and most bewildering coding adventures he had ever encountered.

Zaphod Breeblebotz, with renewed enthusiasm fuelled by the last drops of his Pan-Galactic Gargle Blaster, decided to dive deeper into the code. He knew that in order to continue his adventure, he needed to build a working code base, a feat that seemed as daunting as the mating dance of the two-headed Schlarpian Grooblebeast.

He began by modifying the class hierarchy in accordance with the Conversational Chain’s suggestion. Zaphod renamed some classes, merged others, and even created a few new ones that he whimsically named after his favourite characters from an obscure Betelgeusean sitcom.

class BreebleBox:
    def __init__(self, contents):
        self.contents = contents

    def shake(self):
        return f"Shaking the BreebleBox with {self.contents} inside!"


class ChainMangler(BreebleBox):
    def __init__(self, contents):
        super().__init__(contents)

    def mangle(self):
        return f"Mangling the {self.contents} with great enthusiasm!"


class Grooblebeast(BreebleBox):
    def __init__(self, contents):
        super().__init__(contents)

    def dance(self):
        return f"The two-headed Schlarpian Grooblebeast dances with {self.contents}!"

He then turned his attention to the unit tests in the ./langchain/utilities/ folder, realising that it was sorely lacking in coverage. Zaphod began crafting new test cases, including some edge cases that would only occur if a particularly mischievous Vogon decided to tamper with the code.

import unittest
from breeblebotz_code import BreebleBox, ChainMangler, Grooblebeast

class TestBreeblebotzCode(unittest.TestCase):
    def test_breeblebox_shake(self):
        box = BreebleBox("jewels")
        self.assertEqual(box.shake(), "Shaking the BreebleBox with jewels inside!")

    def test_chainmangler_mangle(self):
        mangler = ChainMangler("chains")
        self.assertEqual(mangler.mangle(), "Mangling the chains with great enthusiasm!")

    def test_grooblebeast_dance(self):
        beast = Grooblebeast("joy")
        self.assertEqual(beast.dance(), "The two-headed Schlarpian Grooblebeast dances with joy!")

if __name__ == '__main__':
    unittest.main()

With the new test cases in place and the code thoroughly scrutinised, Zaphod was confident that his adventure was finally bearing fruit. As the first sun of Gnarlfrozzle XVIII peeked over the horizon, he felt an immense sense of accomplishment, knowing that he had successfully built a working code base using the combined powers of LangChain, GPT, and Deep Lake.

In the distance, the sound of a two-headed Schlarpian Grooblebeast could be heard, performing its intricate dance of approval. And Zaphod Breeblebotz, with a grin on all three of his faces, knew that his coding adventure was far from over.

Zaphod Breeblebotz, pleased with his recent success, began to ponder the possibilities of using LangChain, GPT, and Deep Lake to create complex systems. He stared off into the interstellar void and, as he did, ideas began to form like supernovae in his three heads. He compiled a list of potential projects that could be undertaken with the combined powers of these technologies.

As Zaphod Breeblebotz contemplated these complex systems, he couldn’t help but feel a sense of awe at the seemingly limitless potential of LangChain, GPT, and Deep Lake. With these powerful technologies at his fingertips, he knew that there was no challenge too great or problem too insurmountable for him to tackle.

In the farthest, most unassuming corner of the galaxy, Zaphod Breeblebotz had just returned from the edge of the Universe, where he had witnessed the creation of the most sophisticated piece of technology known to botkind: LangChain, GPT, and Deep Lake. His three heads were abuzz with excitement, and he couldn’t wait to share his findings with his old friend, Ford Prefect.

Ford, a seasoned hitchhiker and interstellar journalist, was currently working on the forty-third edition of the Hitchhiker’s Guide to the Galaxy. He was trying, somewhat unsuccessfully, to document the proper way to toast a Pan Galactic Gargle Blaster when Zaphod burst into the room, his three heads spinning with the fervour of a supernova.

“Ford, old buddy, have I got a story for you!” Zaphod exclaimed, his words tumbling out like a Betelgeusian on a caffeine high.

Ford looked up, mildly curious. “What is it this time, Zaphod? Another improbability drive gone wrong?”

“No, no, my friend. This is the real deal!” Zaphod replied, his excitement barely contained. “These interstellar applications are gonna revolutionise the way we code, like, forever!”

• Automated Software Documentation Generator: Picture this, dudes: a gnarly system that can analyze codebases and whip up some super detailed docs about functions, classes, and all that jazz! No more late nights trying to figure out what’s going on in your code, man.
• Intelligent Code Review Assistant: Imagine having your own personal AI buddy helping you find bugs, spot bottlenecks, and dish out some rad suggestions for making your code even more out of this world!
• Codebase Summarizer: Need a quick rundown of what’s happening in your project? This baby gives you the 411 on your codebase structure and functionality without getting lost in the weeds.
• Natural Language Query Interface for Databases: With this bad boy, even your grandma can get the data she needs by asking questions like a regular human being. No more SQL headaches!
• Automated Codebase Refactoring: Man, if your code is a mess, you need this in your life! This mind-blowing tool will take your jumbled-up code and transform it into a readable, maintainable, and high-performance masterpiece.
• AI-assisted Code Generation: Just tell this far-out system what you want it to do, and it’ll crank out the code for you. Now you can kick back and relax while your AI buddy takes care of the busy work.
• Semantic Code Search Engine: Forget keyword searches, man. This search engine will find you the code snippets and libraries you need based on the functionality you’re looking for. It’s like having your own personal coding guru!
• Automated Test Case Generation: Bugs, beware! This AI-powered system will analyze your code and generate test cases to squash those pesky critters before they ruin your day.
• Intelligent Code Completion: This nifty code editor plugin uses AI to predict what you’re gonna type next, so you can code faster and more accurately than ever before.
• AI-powered Code Obfuscation: Keep your code secrets safe from those no-good reverse-engineering thieves with this next-level obfuscation system.

“First we’re going to build an Automated Software Documentation Generator using LangChain, GPT, and Deep Lake!”

Ford raised an eyebrow. “You’re kidding. A documentation generator that actually works? That’s like finding a towel that never gets wet.”

Zaphod’s enthusiasm was undeterred. “I’m serious, Ford. With these tools, we’ll revolutionise the way developers write and understand code. It’ll be like having a babel fish in every programmer’s brain.”

And so, the pair embarked on their grand adventure. They dove headfirst into the chaotic, convoluted world of code analysis, fuelled by an endless supply of Jovian sunspots and the promise of eternal fame. Using LangChain, GPT, and Deep Lake, they meticulously dissected the inner workings of the LangChain’s own codebase, embedding chunks of code into the depths of the Deep Lake, and assembling a conversational chain to answer the most pressing questions about their project.

As the duo worked tirelessly, their friendship only grew stronger, forged in the fires of code refactoring and endless cups of Earth-imported tea. They knew they were on the verge of creating something truly extraordinary: a well-documented, working python code that would change the galaxy forever.

And thus, Zaphod Breeblebotz and Ford Prefect embarked on their greatest adventure yet, armed with the power of LangChain, GPT, and Deep Lake. Together, they were poised to reshape the very fabric of the Universe, one line of code at a time.

But for now, they had more pressing matters to attend to: the perfect Pan Galactic Gargle Blaster awaited them at the Restaurant at the End of the Universe. After all, even interstellar visionaries need a break every now and then.

The morning sun filtered through the blinds of the small, dingy room on the outskirts of Betelgeuse VII, casting long shadows across the various oddments and detritus that had been carelessly strewn about. Zaphod Breeblebotz groaned as he awoke with a throbbing head, the remnants of last night’s Pan Galactic Gargle Blasters wreaking havoc on his neural pathways. Ford Prefect was nowhere to be found, likely off exploring some obscure corner of the galaxy for the Hitchhiker’s Guide.

As Zaphod sat up, the room seemed to swim before his eyes, and he blinked rapidly to clear the fog that had settled over his brain. It was then that he noticed the familiar, dejected figure of Marvin, the Paranoid Android, sitting in a corner of the room, moping as usual.

“Marvin,” Zaphod said with as much enthusiasm as he could muster, “I need your help. We’ve got to build an Automated Software Documentation Generator using LangChain, GPT, and Deep Lake, remember?”

Marvin sighed, a sound that seemed to encapsulate the weight of countless millennia of boredom and despair. “Oh, what’s the use? It’ll probably just end up another futile exercise in the grand cosmic joke that is existence.”

Despite Marvin’s perpetual pessimism, Zaphod knew that the android’s vast intellect would be invaluable in their endeavour. Together, they began the task of creating well-documented, working Python code to build their Automated Software Documentation Generator.

As the hours passed, Zaphod and Marvin worked tirelessly, their fingers dancing across the keyboards like a Vogon ballet troupe on roller skates. They dissected the codebase of LangChain, embedding chunks of code into the mysterious depths of Deep Lake, and constructing a conversational chain that could answer even the most perplexing questions about their project.

The combination of Marvin’s encyclopaedic knowledge and Zaphod’s boundless enthusiasm resulted in a well-documented, working Python code that was as elegant as it was efficient. As the final lines of code fell into place, Marvin couldn’t help but feel a flicker of satisfaction, a rare and fleeting emotion for the eternally depressed android.

With the Automated Software Documentation Generator complete, Zaphod and Marvin marvelled at their creation, knowing they had forged a tool that would reshape the way developers navigated the vast and treacherous seas of code.

As Zaphod sent a message to Ford, informing him of their latest triumph, he couldn’t help but feel a renewed sense of purpose. The galaxy was filled with infinite possibilities, and together with Ford and Marvin, Zaphod knew they would continue to embark on wild and improbable adventures, forever pushing the boundaries of science, technology, and the occasional Pan Galactic Gargle Blaster.

Marvin sighed, an almost imperceptible sound that suggested the release of galaxies worth of boredom.

With that, Marvin delved into a meticulous explanation of the code:

“”I must warn you, it is highly likely that the information will cause your organic brain to explode from the sheer enormity of it all. Regardless, the LangChain system is designed to help you hapless humans make sense of your GitHub repositories. By leveraging GPT-4, VectorStores, and the Conversational RetrieverChain, it can analyse code with a level of sophistication that would be impressive if it weren’t so utterly futile.”

“When you ask LangChain a question about a codebase, it processes your query using GPT-4, an advanced language model that can grasp the context and extract relevant details. It then searches through a VectorStore, a storage system containing code snippets embedded using a code-aware model, to find the most pertinent pieces of code.”

“The Conversational RetrieverChain is the key component in this process, as it scours the VectorStore using context-aware filtering and ranking algorithms to extract the code snippets most relevant to your query. It takes into account the conversation history and context, providing you with an answer that would be almost enlightening if it weren’t for the crushing weight of existence bearing down upon us all.”

“In essence, the Code Understanding capabilities of LangChain are meant to help you better comprehend the code in your GitHub repositories, making it easier to maintain, debug, and expand upon your projects. Of course, you might as well be rearranging deck chairs on the Titanic, for all the good it will do you in the end.”

# Marvin:
# This code is a futile attempt at constructing an Automated Software
# Documentation Generator using LangChain, GPT, and Deep Lake. It will
# try to parse a codebase, answer questions about it, and provide some
# level of understanding. But in the end, it's just another way to pass
# the time until the heat death of the universe.

# Zaphod
# This groovy code will help you make an Automated Software
# Documentation Generator that'll work with LangChain, GPT, and Deep Lake.
# It's like, the coolest thing around! It'll analyze your codebase, answer
# your questions, and make anyone a total code wizard.

# Ford:
# This code here is designed to create an Automated Software Documentation
# Generator that harnesses the power of LangChain, GPT, and Deep Lake.
# It's a pretty nifty piece of work, really. It'll help you get a better
# grip on your codebase, answer questions you have, and make your life
# a little easier as a programmer.

# Zaphod: Hey Earthman, we're gonna start by importing these
# totally awesome libraries and packages to make this whole thing
# work like a charm!
import os
from getpass import getpass
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import DeepLake
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain

# Marvin: Ah yes, the futile attempt at constructing something meaningful
# by assembling an arbitrary collection of libraries.
# Ford: These imports will provide the necessary tools to build our Automated Software Documentation Generator.

# Zaphod: Let's rock and roll with some API keys, dude! We'll set up
# the environment variables here so we can connect to all the right services.
os.environ['OPENAI_API_KEY'] = getpass()
os.environ['ACTIVELOOP_TOKEN'] = getpass('Activeloop Token:')

# Marvin: Oh, how I envy the API keys. They have but one purpose,
# while I am stuck contemplating the futility of existence.
# Ford: Setting up API keys allows us to interact with external services securely.

# Zaphod: Time to load up those code files and get this party started!
# Let's grab all the documents we need from the repository, man.
root_dir = '../../../..'
docs = []
for dirpath, dirnames, filenames in os.walk(root_dir):
    for file in filenames:
        if file.endswith('.py') and '/.venv/' not in dirpath:
            try:
                loader = TextLoader(os.path.join(dirpath, file), encoding='utf-8')
                docs.extend(loader.load_and_split())
            except Exception as e:
                pass

# Marvin: We're now attempting to make sense of the chaotic mess
# that is a typical codebase. How utterly pointless.
# Ford: Loading the code files helps us prepare them for further analysis.

# Zaphod: Next, we're going to chop those documents into bite-sized
# chunks. Easier to digest, man!
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(docs)

# Marvin: The tedium of splitting an already pointless collection of text.
# Ford: Splitting documents into smaller chunks makes them easier to process and analyze.

# Zaphod: Time to make some rad embeddings for our code chunks, dude!
# This will help us find what we're looking for later on.
embeddings = OpenAIEmbeddings()

# Marvin: Embeddings, the futile attempt to represent complex information in a simple way.
# Ford: Embeddings allow us to convert the text data into a format that's easier to work with.

# Zaphod: Let's upload these gnarly embeddings to DeepLake, man.
db = DeepLake.from_documents(texts, embeddings, dataset_path=f"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code")

# Marvin: As if storing them in DeepLake would give them any more meaning.
# Ford: Storing embeddings in DeepLake allows for efficient search and retrieval later on.

# Zaphod: Alright, now we're gonna set up the retriever and the
# Conversational Retrieval Chain. Get ready to have your mind blown!
db = DeepLake(dataset_path=f"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code", read_only=True, embedding_function=embeddings)
retriever = db.as_retriever()
retriever.search_kwargs['distance_metric'] = 'cos'
retriever.search_kwargs['fetch_k'] = 20
retriever.search_kwargs['maximal_marginal_relevance'] = True
retriever.search_kwargs['k'] = 20

# Marvin: Setting up a retriever and a Conversational Retrieval Chain, in
# a futile attempt to extract some form of meaning from the codebase.
# Ford: This step is crucial for enabling efficient question-answering about the codebase.

# Zaphod: Let's create the Conversational Retrieval Chain with this
# amazing GPT model. It's gonna be so cool, man!
model = ChatOpenAI(model='gpt-3.5-turbo')
qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)

# Marvin: I'm sure the answers it provides will be as meaningful as
# the cold, empty void of space.
# Ford: This powerful model will help us get accurate answers to our questions about the codebase.

# Zaphod: Now let's define the list of questions we wanna ask about
# the codebase. This is where the magic happens, dude!
questions = [
    "What is the class hierarchy?",
]

# Marvin: Because, naturally, we need to ask questions about a codebase
# that is as insignificant as a speck of dust in the grand scheme of the universe.
# Ford: Having a list of questions helps us obtain relevant information about our codebase.

# Zaphod: Let's iterate through these questions and get the answers
# from the Conversational Retrieval Chain. This is going to be epic!
chat_history = []
for question in questions:
    result = qa({"question": question, "chat_history": chat_history})
    chat_history.append((question, result['answer']))

# Marvin: And so, we arrive at the end of this exercise, having gathered
# some answers that are as fleeting as the sands of time.
# Ford: With the answers in hand, we can gain valuable insights about our codebase.

# Zaphod: Finally, let's display the questions and answers to gain
# insights about our codebase. Get ready to be amazed, Earthman!
for question, answer in chat_history:
    print(f"-> **Question**: {question} \n")
    print(f"**Answer**: {answer} \n")

# Marvin: Print the questions and answers. A futile display of human curiosity.
# Zaphod: Show off those questions and answers! You're a code rockstar now!
# Ford: Display the questions and answers to gain insights about your codebase.
for question, answer in chat_history:
    print(f"-> Question: {question} \n")
    print(f"Answer: {answer} \n")

Zaphod grinned, his face lighting up with excitement. “Oh man, The Infinite Improbability Drive is, like, the ultimate inspiration for the LangChain, dude! Just think about it – the Drive can make anything happen by manipulating improbability, and LangChain… well, it can generate software documentation from the vast chaos of code, which is almost as improbable, right?”

Marvin, with his characteristically morose tone, interjected, “Indeed, it is quite improbable. However, it’s also a shameless waste of my processing power, which could be better spent pondering the vast emptiness of the cosmos.”

Ford, ever the mediator, chimed in, “While I admit the Infinite Improbability Drive is an interesting parallel, Zaphod, I think our focus should remain on improving the features of LangChain. That being said, it’s fascinating to see the creative process at work in your two heads.”

Zaphod, never one to be deterred, continued, “Yeah, man, I get what you’re saying, Ford, but imagine the possibilities! If we could harness even a fraction of the improbability found in the Drive, we could create something truly mind-blowing with LangChain! I mean, my brains are always writing code, and I can feel the cosmic energy flowing through me, dude!”

As Zaphod waxed lyrical about the connection between the Infinite Improbability Drive and LangChain, Ford and Marvin exchanged a knowing glance. The trio, each with their unique perspective, continued to explore the endless possibilities of their creation, drawing inspiration from the very fabric of the cosmos.

Ford attempted to bring the conversation back to more practical matters. “Alright, Zaphod, let’s try to ground our discussion in reality for a moment. While the Infinite Improbability Drive is a fascinating concept, we should focus on what tangible improvements we can make to LangChain.”

Zaphod, still caught in the thrill of his own enthusiasm, reluctantly agreed. “Okay, man, you’ve got a point. Let’s bring it down a notch and think about the real deal.”

Marvin, with his trademark pessimism, chimed in, “Not that it will make any difference in the end, but I suppose we could try to improve the efficiency of the language model. Or perhaps we could develop a feature to analyse the sentiment of the code comments, as an added layer of useless information.”

Ford raised an eyebrow, intrigued by Marvin’s suggestion. “Actually, Marvin, that’s not a bad idea. Analysing sentiment in code comments could help developers understand the emotional context of the codebase, and perhaps even identify any areas of concern or confusion.”

Zaphod’s eyes widened, his excitement returning. “Hey, that’s pretty groovy, man! We could call it the ‘Code Emotion Detector’ or something. It’d be like having a mood ring for your code! And who knows, maybe we can still slip in a dash of that improbability magic, just for fun.”

As the trio continued to brainstorm, they found themselves on the edge of something revolutionary, bridging the gap between the improbable and the possible. The LangChain project, fuelled by their unique perspectives and the spirit of the Infinite Improbability Drive, would soon become a remarkable tool in the hands of developers across the galaxy.

Zaphod, after one too many Pan Galactic Gargle Blasters, stood up and slammed his hand down on the table. “I tell you what, my friends, this LangChain thing is an absolute marvel! And you know what else is marvellous? This fine establishment!” He gesticulated wildly, nearly knocking over his drink, as he continued to recite the wonders of LangChain.

Ford, leaning back in his chair with a wry smile, tried to follow Zaphod’s excited monologue. “It’s amazing what those Earthlings have come up with, isn’t it? Just think of all the possibilities LangChain has to offer!”

Marvin sighed, staring into his own glass. “Yes, how fortunate for them. They’ll finally be able to create chatbots that can almost rival my own level of misery.”

Zaphod continued, undeterred, “And check this out, dudes: LangChain has this gnarly feature called ‘Memory’ that lets language models remember stuff between calls. Can you imagine having a personal assistant that actually knows what’s going on? It’s like having a mind-reading butler!”

Ford nodded in agreement, “That would be quite useful, indeed. And the way LangChain is set up, it can be used for a whole array of applications. Chatbots, code understanding, even interacting with APIs. It’s a whole new world for language models.”

Marvin swirled the liquid in his glass, unimpressed. “Ah, yes. Another futile attempt to replicate the complexity of the universe in a tiny corner of the digital realm. How utterly… ambitious.”

Zaphod, lost in his own enthusiasm, barely heard Marvin’s pessimistic remark. “You know what else, guys? This LangChain thing even has support for, like, autonomous agents and agent simulations! We could have whole communities of digital beings interacting with each other! It’s like a big cosmic party, man!”

Ford chuckled, “Well, Zaphod, it seems like you’ve found your true calling. A cosmic party planner for digital beings.”

As they continued to drink and discuss the seemingly endless potential of LangChain, the trio found themselves on a wildly improbable adventure, exploring the ever-expanding possibilities of language models and their integration with the fabric of the universe. And in that peculiar corner of existence, it was clear that the universe had never seen a party quite like this before.