minhbtrc / langchain-chatbot

This code is an implementation of a chatbot using LLM chat model API and Langchain.

Chatbot with Langchain, LangSmith.

Requirement

• Python version >= 3.9. Because langchainhub package require it

Description

• This is a chatbot implementation with Langchain framework.
  • Base LLM: Vertex AI or OpenAI API
  • Memory: MongoDB
  • UI:
    • Gradio
    • Langchain UI: Chat Langchain
      • Use it to leverages LangChain's streaming support.
  • Prompt versioning and tracing: LangSmith
• User can custom bot's personality by setting bot information like gender, age, ...
• Demo UI:

PII for chatbot

• Data anonymization with Microsoft Presidio
• To protect personally identifiable information (PII), we add PresidioAnonymizer to my bot to replace PIIs before pass to LLM api. View code in Anonymizer
• Steps when using it:

  • User message after anonymize:

    

  • Anonymized prompt before input to LLM:

    

  • De-anonymized response to user after LLM call:

    

How to use

• You need Google Cloud credentials to call Vertex API or OPENAI API KEY to call OpenAI API
• You need create MongoDB database and collection to use as Langchain memory

Setup tracing with Langsmith

• Langsmith docs: LangSmith
• Configure environment to connect to LangSmith.

  export LANGCHAIN_TRACING_V2=true
  export LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
  export LANGCHAIN_API_KEY="<your-api-key>"
  export LANGCHAIN_PROJECT="chatbot-with-langchain"
  

Running

0. Download the models for the languages to use in anonymizer. PII support.
   1. python -m spacy download en_core_web_md
1. RUN backend
   1. Clone repo: git clone https://github.com/btrcm00/chatbot-with-langchain.git
   2. Add google-cloud-platform credential file to secure/vertexai.json
   3. cd chatbot
   4. Install required packages: pip install -r requirements.txt
   5. Create MongoDB database and config environment variables to connect Mongo.
   6. Run: python app.py
2. RUN frontend
   1. cd chatbot_frontend
   2. Install packages: npm i
   3. Start frontend: npm start dev