Hacking LangChain For Fun and Profit - I

1 Overview

Recently I’ve looked into the LangChain project and I was surprised by how it could be such a powerful and mature a project built in such short span of time. It covers many essential tools for creating your own LLM-driven projects, abstracting cumbersome steps with only a few lines of code.

I like where the project direction is going, and the development team has been proactively including and introducing new ideas of the latest LLM features in the project.

The path to understanding this new project weren’t really smooth. It has its own opinions for code organization and it could be unintuitive to guess how to hack your own projects for more than the tutorials. Many of the tutorials out there explains how to create a small application with LangChain but doesn’t cover how to intuitively comprehend the abstraction and design choices.

Hence I have taken the initiative to document my personal cognitive process throughout this journey. By doing so, I aim to clarify my own understanding while also providing assistance to y’all who are interested in hacking LangChain for fun and profit.

This blog post will dedicate to the overall understanding of all the concepts. I found it really helpful to start by understanding the concepts that directly interacts with the LLM, especially the core API interfaces. Once you have the mindmap of all the LangChain abstractions, it’s much more intuitive to hack and extend your own implementation.

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Paper Readings on LLM Task Performing

1 Overview

I’ve spent the last couple of months reading about the development of AI and NLP development in general ever since the release of ChatGPT. And here’s some of my personal findings specifically on task performing capabilities.

The field of AI has been advancing rapidly, and the results have exceeded expectations for many users and researchers. One particularly impressive development is the Language Model (LLM), which has demonstrated a remarkable ability to generate natural, human-like text. Another exciting example is ChatGPT from OpenAI, which has shown impressive task performing and logical reasoning capabilities.

Looking ahead, I am optimistic that LLM will continue to be incredibly effective at performing more complex tasks with the help of plugins, prompt engineering, and some human input/interactions. The potential applications for LLM are vast and promising.

I’ve compiled a list of papers of extending the task performing capabilities in this field. I’m quite enthusiastic and excited about the potential of longer term of this capability that brings LLMs like ChatGPT to more powerful applications.

Here’s my first list of paper, also what I consider to be more fundamental papers, along with my very quick summaries.

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OpenAPI Generator For Go Web Development

The Openapi Generator for Go API and Go web app development works surprisingly well, but somehow I found that it’s not so often mentioned. Recently I’ve tried it in one of my projects, and in my (limited) experience with it, I was pleasantly surprised by how good it was. With some setup, it could generate Go code with decent quality, and it’s fairly easy to use once you get a hang of it. Whether you’re building a standalone web-app from scratch or creating a service with REST API endpoints, openapi-generator might come up handy for you.

Using a generator might save much time to kick-start your web app project. And most importantly, I found that a good, well-defined, consistent API definition is so crucial to your development, testing, and most importantly, communication among teams and customers. I highly recommend that for any sizable project, you spend some quality time on writing a good API spec. It’ll become essential to your development workflow. I used to highly doubt this, and now I don’t think I can live without it.

And if you manually keep documentation, or API specifictions in sync with your Go code, you’ll have a hard time reviewing, checking, and testing between code and specs. The best way IMHO is to automate the process, by either generating the API code from spec, or the other around. Many toolings support either one of these, and openapi-generator is one of the really nice tools that I’m going to introduce in this blog post.

Openapi Generator supports many languages on the server as well as on the client side. And it has generator for different frameworks of Go. Right here I’m going to use go-server generator as an example. It uses the Gorilla framework for the server-side code.

For this blog post I’ve also made an example of code generation in my Github repo. I’ve generated the code, and implemented only one endpoint /books with example data:

https://github.com/hxy9243/go-examples/tree/main/src/openapi

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Reading BBoltDB

By trying to understand databases in general, I’ve recently dived into the source code of BBoltDB for a deeper understanding of its implementation. It’s a fairly small, nifty local KV store for source-code reading and studying, especially you’re a Golang programmer. At the same time it’s widely used and tested, embeded as the powerful storage engine for systems like Etcd and Consul. Though it’s only backed by one file, it’s capable of serving data size up to TB level.

Below is my notes for the very quick reading from its source code, hopefully providing anyone interested in data systems a quick glimpse of what’s under the hood. It’s not meant as an exhaustive and deep analysis.

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Building Applications With Cassandra: Experience And Gotchas

Recently I’ve summarized some experience on quickly getting started with Cassadra. And for this post I’d like to keep writing about some of our experience using and operating Cassandra. Hopefully it could be useful to you, and help you avoid future unwanted surprises.

Election and Paxos

Cassandra is always considered to be favoring the “AP” in “CAP” theorem, where it guarantees eventual consistency for availability and performance. But when really necessary, you can still leverage Cassandra’s built-in “Light-weight Transaction” for elections to determine a leader node in the cluster.

Basically, it works by writing to a table with your own lease:

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INSERT INTO leases (name, owner) VALUES ('lease_master', 'server_1') IF NOT EXISTS;

The IF NOT EXISTS triggers the Cassandra built-in Light-weight Transaction and can be used to declare a consensus among a cluster. With a default TTL in the table, this can be used for leases control, or master election. For example:

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CREATE table leases (
name text,
owner text,
) WITH default_time_to_live = 16;

So that the lease owner needs to keep writing to the lease row for heartbeats.

I’m not sure about the performance characteristics of Cassandra’s election behavior with other applications (etcd, Zookeeper, …) and it’ll be interesting to see a study. But since those are already more full-featured and well-understood in keeping consensus, I’d recommend delegating this behavior to them unless you’re stuck with Cassandra for your application.

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Building Applications With Cassandra: A Very Quick Guide

Cassandra Overview

Cassandra as an open-source NoSQL database has gained popularity in cloud and big data applications. Inspired by DynamoDB, it also has good latency, tunable consistency, easy to achieve scalability, and high-availability with cluster setup.

Our team’s been using Cassandra as the backend for an application we’ve been shipping to customers. We chose it for its high-availability setup, and good performance. We used to store time-series data and some simple configuration data as Key-value pairs. So it felt like a natural choice. And in our experience over time, it has proven to be highly capabable at serving our purposes.

With impressive availability, scalability, and read/write performance, Casandra also comes with its limitations. We cannot design data models the same way we did with traditional relational databases with SQL interface. And it doesn’t come with many of the guarantees from traditional databases, like consistency level, transactions, cascading deletion, etc. Like other NoSQL databases, Cassandra was designed to optimize batch write operations with good read and write latency. It fits applications without too much update/delete operations, especially ones with no high amounts of transactions.

So the best use cases for Cassandra can be:

  • You have a high volume of data with availability concerns.
  • Most data is sequential read/write or append, e.g.: logs, time-series, IoT applications, track records, messages, etc.
  • You don’t have complex data relations between data entities that requires high amount of transactions.

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Paper Reading: Ray: A Distributed Framework for Emerging AI Applications

Paper link: https://www.usenix.org/system/files/osdi18-moritz.pdf

Overview

Ray is a new and grossing distributed programming framework, with an ambitious plan to be the foundation of emerging AI/ML applications. In its own words, it aims to “provide a universal API for distributed computing”. Which means it needs to provide a programming interface that’s flexible enough for new applications, and a backend system designed to scale for elastic computing needs with some good performance. This paper (OSDI 18’) explains its API and architecture design to fulfill this goal. And I’ve found some very interesting points.

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Golang Channel Idioms

While learning Golang, I was fascinated with the power Golang’s goroutines and channels. Channel is a powerful tool to tackle synchronization problems in asynchronous programs. It acts as a bridge between async goroutines and can describe some complicated logic expressively. Together they can be powerful weapons in building async applications. On the other hand, when misused, they can be a nightmare to debug.

Here I’ve summarized a few of the valuable idioms of using Golang routines from multiple references as well as my own experience. They can serve as a helpful toolbox that comes in handy for similar problems. So that you don’t have to design them from scratch, which might help you avoid synchronization errors.

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Paper Reading: 150 Successful Machine Learning Models Deployed: 6 Lessons Learned At Booking.com

Paper link: https://www.kdd.org/kdd2019/accepted-papers/view/150-successful-machine-learning-models-6-lessons-learned-at-booking.com

Or download.

First published in KDD from booking.com, the paper described its lessons from deploying Machine Learning models in their production service. It provided some intriguing insights. I believe many are very valuable to understanding applying Machine Learning in real-world scenarios.

Here are some of my takeaways.

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