LLMs, or Large Language Models, are advanced artificial intelligence models designed to process and generate human-like text by analyzing and learning patterns from vast amounts of textual data.

By Jessie Angelica, Aaron Handoko, and Fakhri Darmawan, Solution Architects Alibaba Cloud Indonesia

Introduction

LLMs, or Large Language Models, are advanced artificial intelligence models designed to process and generate human-like text by analyzing and learning patterns from vast amounts of textual data. One of the remarkable capabilities of LLMs is their adaptability to various language-related tasks, including Text Generation, Language Translation, Text Summarization, and Question Answering.

LangChain is a tool that helps software developers create agents, which are like virtual problem-solvers. These agents can break down big problems into smaller tasks and work with Large Language Models. Our chatbot, for example, uses LangChain to access user data through the app API, analyze the data, and respond to user questions. There's a special agent that reads a file called API YAML, turns its information into tools, and plans how to use these tools to give the best human-like answers. Then, it goes ahead and calls the necessary APIs, analyzes the data, and provides the user with the best response.

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LangChain offers versatile solutions for a wide range of industries. In e-commerce, it excels in providing personalized product recommendations, enhancing the shopping experience for potential customers. In healthcare, LangChain facilitates seamless access to clinical data, streamlining information retrieval processes for medical professionals. Additionally, in government, financial, and banking sectors, it proves invaluable by efficiently retrieving relevant documents, aiding in compliance and decision-making processes.

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Resource Required:

Upload local knowledge file and generate embeddings using SGPT-125M model.
Store embeddings in AnalyticDB for vector retrieval and real-time data warehousing service
Sent to LLM model service deployed on PAI-EAS for real-time question answering

Steps:

1). Cloning from this GitHub
https://github.com/aigc-apps/PAI-Chatbot-Langchain.git
2). Edit the config.json and adjust the configuration with yours. The telegram’s token will generated by BotFather.

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3). Cloning this GitHub and change the previous main.py to the new one
https://github.com/jessieangelica/llmlangchain.git

4). Put your document based on your needs to the docs file

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5). Create the Local Environment using Conda
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6). Create the Docker Environment
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7). Run the code
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Result:

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Case Study:

1). In the insurance sector, LangChain facilitates comprehensive document analysis, ensuring speedy completion of claims through efficient data processing. Moreover, it enables in-depth customer analysis, examining demographics, behaviors, and preferences to enhance targeted sales strategies.

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2). For OJK, LangChain excels in analyzing vast financial data and intricate regulations, interpreting complex documents, and ensuring compliance with evolving standards. Its adaptability and processing capabilities empower organizations in the insurance industry and regulatory bodies to streamline operations, make informed decisions, and stay ahead in the dynamic landscape of finance and insurance.

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Community

Smart Talk: Empowering Conversations with LLM Langchain AI Chatbots

Introduction

Steps:

Result:

Case Study:

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