Build an AI-based Personal Financial Advisor with LangChain

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Introduction

Managing personal finances is a cumbersome process. Receipts, bank statements, credit card bills, and expense records accumulate quickly. Despite our best intentions, many of us often struggle to maintain a clear overview of our financial health. It's not uncommon to overlook expenses or procrastinate on updating our budgets. Inevitably, this leads to financial surprises and missed opportunities for financial growth.

Even when we diligently track our expenses, the data can be overwhelming. Analyzing every transaction to identify patterns, pinpoint areas for improvement, and set financial goals is no easy feat. It's challenging to answer questions like, "Am I spending too much on entertainment?" or "Is my investment portfolio well-balanced?" or "Should I cut back on dining out?" or "Do I need to limit socializing with friends?" and "Is it time to increase my investments?"

Imagine having a personal assistant capable of automating these financial tasks, effortlessly transforming your transaction history into valuable insights. What if, at the end of each month, you received comprehensive financial analyses and actionable recommendations? Thanks to the rapid advancements in generative AI, this dream has become a reality, made possible by the incredible capabilities of LLM. No more endless hours of spreadsheet tinkering or agonizing over budgeting apps.

In this article, we'll delve into the development of a personal financial advisor powered by LangChain. This virtual assistant will not only automate the tracking of your finances but also provide tailored recommendations based on your unique spending patterns and financial goals.

Building an AI-powered personal financial advisor is an exciting endeavor. Here's an overview of how the personal financial advisor operates:

1. Data Input: Users upload their personal transaction history, which includes details of income, expenses, investments, and savings.
2. Data Processing: LangChain with LLM in the backend will process the data, categorize expenses, identify trends, and compare your financial activity with your goals and benchmarks.
3. Financial Analysis: The advisor generates a detailed financial analysis report, highlighting key insights such as spending habits, saving potential, and investment performance.
4. Actionable Recommendations: The advisor will also provide actionable recommendations for the user. It can suggest adjustments to your budget, recommend investment strategies, and even propose long-term financial plans.

The benefits of having an AI-powered personal financial advisor are numerous:

1. Time-Saving: No more tedious data entry and manual budget tracking. The advisor handles it all, giving you more time for what matters most in your life.
2. Personalized Insights: The advisor tailors recommendations based on your unique financial situation, ensuring they align with your goals and aspirations.
3. Financial Confidence: With regular updates and guidance, you gain a better understanding of your financial health and feel more in control of your money.
4. Long-Term Planning: The advisor’s ability to provide insights into long-term financial planning ensures you're well-prepared for your future.

Without wasting any more time, let’s take a deep breath, make yourselves comfortable, and be ready to learn how to build your AI-based personal financial advisor with LangChain!

What is LangChain?

LangChain is developed to harness the incredible potential of LLM, LangChain enables the creation of applications that are not only context-aware but also capable of reasoning, all while maintaining a user-friendly and modular approach.

LangChain is more than just a framework; it's a paradigm shift in the world of language model-driven applications. Here's a closer look at what makes LangChain a transformative force:

1. Context-Aware Applications: LangChain empowers applications to be context-aware. This means that these applications can connect to various sources of context, such as prompt instructions, few-shot examples, or existing content, to enhance the depth and relevance of their responses. Whether you're seeking answers or recommendations, LangChain ensures that responses are firmly grounded in context.
2. Reasoning Abilities: One of LangChain's standout features is its ability to reason effectively. It leverages language models to not only understand context but also make informed decisions. These decisions can range from determining how to answer a given question based on the provided context to deciding what actions to take next. LangChain doesn't just provide answers; it understands the "why" behind them.

Why LangChain?

The power of LangChain lies in its value propositions, which make it an indispensable tool for developers and businesses looking to harness the potential of language models:

1. Modular Components: LangChain offers a comprehensive set of abstractions for working with language models. These abstractions are not only powerful but also modular, allowing developers to work with them seamlessly, whether they're using the entire LangChain framework or not. This modularity simplifies the development process and promotes code reuse.
2. Off-the-Shelf Chains: LangChain provides pre-built, structured assemblies of components known as "off-the-shelf chains." These chains are designed for specific high-level tasks, making it incredibly easy for developers to kickstart their projects. Whether you're a seasoned AI developer or a newcomer, these pre-configured chains save time and effort.
3. Customization and Scalability: While off-the-shelf chains are fantastic for quick starts, LangChain doesn't restrict you. The framework allows for extensive customization, enabling developers to tailor existing chains to their unique requirements or even create entirely new ones. This flexibility ensures that LangChain can accommodate a wide range of applications, from simple chatbots to complex AI systems.

LangChain isn't just a run-of-the-mill framework; it's a versatile toolkit designed to empower developers to create sophisticated language model-powered applications. At the heart of LangChain is a set of interconnected modules, each serving a unique purpose. These modules are the building blocks that make LangChain a powerhouse for AI application development.

1. Model I/O: At the core of LangChain's capabilities is its ability to interact seamlessly with language models. This module facilitates communication with these models, enabling developers to leverage their natural language processing prowess effortlessly.
2. Retrieval: LangChain recognizes that real-world applications require access to relevant data. The Retrieval module allows developers to integrate application-specific data sources into their projects, enhancing the context and richness of responses.
3. Chains: Building upon the previous modules, Chains bring structure and order to the development process. Developers can create sequences of calls, orchestrating interactions with language models and data sources to achieve specific tasks or goals.
4. Agents: Let chains choose which tools to use given high-level directives. Agents take the concept of automation to a new level. They allow chains to make intelligent decisions about which tools to employ based on high-level directives. This level of autonomy streamlines complex processes and enhances application efficiency.
5. Memory: Memory is vital for continuity in applications. This module enables LangChain applications to remember and retain their state between runs of a chain, ensuring a seamless user experience and efficient data handling.
6. Callbacks: Transparency and monitoring are critical aspects of application development. Callbacks provide a mechanism to log and stream intermediate steps of any chain, offering insights into the inner workings of the application and facilitating debugging.

Building the Personal Financial Advisor

Let’s start building our personal financial advisor with LangChain! For the sake of simplicity, let’s consider only three data sources: monthly credit card statements, bank account statements, and cash expense logs. The following is an example of the data format for each of the sources.

 ##
Monthly Credit Card Statement
 
Date: 2023-09-01
Description: Grocery Store
Amount: $150.00
Balance: $2,850.00
 
Date: 2023-09-03
Description: Restaurant Dinner
Amount: $50.00
Balance: $2,800.00
 
Date: 2023-09-10
Description: Gas Station
Amount: $40.00
Balance: $2,760.00
 
Date: 2023-09-15
Description: Utility Bill Payment
Amount: $100.00
Balance: $2,660.00
 
Date: 2023-09-20
Description: Salary Deposit
Amount: $3,000.00
Balance: $5,660.00
 
Date: 2023-09-25
Description: Online Shopping
Amount: $200.00
Balance: $5,460.00
 
Date: 2023-09-30
Description: Investment Portfolio Contribution
Amount: $500.00
Balance: $4,960.00
 
##
Bank Account Statement
 
Date: 2023-08-01
Description: Rent Payment
Amount: $1,200.00
Balance: $2,800.00
 
Date: 2023-08-05
Description: Grocery Store
Amount: $200.00
Balance: $2,600.00
 
Date: 2023-08-12
Description: Internet and Cable Bill
Amount: $80.00
Balance: $2,520.00
 
Date: 2023-08-15
Description: Freelance Gig Income
Amount: $700.00
Balance: $3,220.00
 
Date: 2023-08-21
Description: Dinner with Friends
Amount: $80.00
Balance: $3,140.00
 
Date: 2023-08-25
Description: Savings Account Transfer
Amount: $300.00
Balance: $3,440.00
 
Date: 2023-08-29
Description: Online Shopping
Amount: $150.00
Balance: $3,290.00
 
##
Cash Expense Log
 
Date: 2023-07-03
Description: Coffee Shop
Amount: $5.00
Balance: $95.00
 
Date: 2023-07-10
Description: Movie Tickets
Amount: $20.00
Balance: $75.00
 
Date: 2023-07-18
Description: Gym Membership
Amount: $50.00
Balance: $25.00
 
Date: 2023-07-22
Description: Taxi Fare
Amount: $30.00
Balance: -$5.00 (Negative balance indicates a debt)
 
Date: 2023-07-28
Description: Bookstore
Amount: $40.00
Balance: -$45.00
 
Date: 2023-07-30
Description: Cash Withdrawal
Amount: $100.00
Balance: -$145.00

To create our personal financial advisor, we’ll use the chat model interface provided by LangChain. There are several important components to build a chatbot with LangChain:

1. `chat model`: Chat models are essential for creating conversational chatbots. These models are designed to generate human-like responses in a conversation. You can choose between chat models and LLMs (Large Language Models) depending on the tone and style you want for your chatbot. Chat models are well-suited for natural, interactive conversations.
2. `prompt template`: Prompt templates help you construct prompts for your chatbot. They allow you to combine default messages, user input, chat history, and additional context to create meaningful and dynamic conversations. Using prompt templates makes it easier to generate responses that flow naturally in a conversation.
3. `memory`: Memory in a chatbot context refers to the ability of the bot to remember information from previous parts of the conversation. This can be crucial for maintaining context and providing relevant responses. Memory types can vary depending on your use case, and they can include short-term and long-term memory.
4. `retriever` (optional): Retrievers are components that help chatbots access domain-specific knowledge or retrieve information from external sources. If your chatbot needs to provide detailed, domain-specific information, a retriever can be a valuable addition to your system.

First, we need to set the API key for our LLM. We’ll use OpenAI in this example.

import os
os.environ["OPENAI_API_KEY"] = “your openai key”

Then, we can simply load the necessary chat modules from LangChain.

 from langchain.schema import (
    AIMessage,
    HumanMessage,
    SystemMessage
)
from langchain.chat_models import ChatOpenAI

The ChatOpenAI is the main class that connects with the OpenAI LLM. We can pass `HumanMessage` and `SystemMessage` to this class and it will return the response from the LLM in the type of `AIMessage`.

chat = ChatOpenAI(model_name=”gpt-3.5-turbo”)
messages = [SystemMessage(content=prompt),
                     HumanMessage(content=data)]
chat(messages)

Let’s see the following example where we pass the prompt along with the data and the LLM returns the response via the ChatOpenAI object.

Boom! We just got our first analysis and recommendation from our personal financial advisor. This is a very simple example of how to create our personal financial advisor. Of course, there’s still a lot of room for improvement. For example, currently, we need to pass manually the relevant data sources as the HumanMessage. However, as mentioned before, LangChain provides a built-in class to perform retrieval. This means that we can just create another script to automatically dump all of the relevant data into some sort of document or even database, and then LangChain can directly read the data directly from there. Hence, we can get automated reports every month without needing to manually input the relevant data.

Conclusion

Congratulations on keeping up to this point! Throughout this article, you have learned what is LangChain, what it is capable of, and how to build a personal financial advisor with LangChain. Hope the best for your experiment in creating your personal financial advisor and see you in the next article!

Author Bio

Louis Owen is a data scientist/AI engineer from Indonesia who is always hungry for new knowledge. Throughout his career journey, he has worked in various fields of industry, including NGOs, e-commerce, conversational AI, OTA, Smart City, and FinTech. Outside of work, he loves to spend his time helping data science enthusiasts to become data scientists, either through his articles or through mentoring sessions. He also loves to spend his spare time doing his hobbies: watching movies and conducting side projects.

 Currently, Louis is an NLP Research Engineer at Yellow.ai, the world’s leading CX automation platform. Check out Louis’ website to learn more about him! Lastly, if you have any queries or any topics to be discussed, please reach out to Louis via LinkedIn.