Home/Inference & Serving/langchain-chatbot
langchain-chatbot logo

langchain-chatbot

minhbtrc/langchain-chatbot

Implementation of a chatbot using LLM chat model API and Langchain

GraphCanon updated 1w · GitHub synced 1w

62 stars11 forksLast push 1y Python MIT

Decision brief

Langchain-chatbot is notable for its integration with GPT-4 and Langchain framework to offer features such as personality customization and data anonymization.

Good fit when

  • - When you need a chatbot solution that integrates with the GPT-4 model for more advanced AI interactions
  • - If your project requires customizing personalities in the chatbot, offering a tailored user experience

Avoid when

  • - In scenarios where you prefer not to use or cannot utilize OpenAI APIs due to regional restrictions or preferences for open-source alternatives
  • - For projects that do not require real-time streaming responses, since Langchain-chatbot provides this feature which might not be necessary in some applications

Observed Jul 14, 2026 · Source: enrich:decision_facts

Verify the decision

Maintenance and security

Full trust report
Maintenance
Dormant (507d since push)
As of 1w
Provenance
Not a fork · Personal account
As of 1w
Security (OSV)
No lockfile
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install langchain-chatbot
PyPI

Similar tools

Same-category neighbours. No typed graph edges are catalogued for this tool yet.

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A chatbot project with support for GPT-4 that uses Langchain framework and incorporates features such as personality customization and data anonymization.

Capability facts

Deploy
Self-host

Source: dockerfile:Dockerfile · Aug 15, 2026

Docker
Dockerfile present

Source: dockerfile:Dockerfile · Aug 15, 2026

Languages
python

Source: github.language · Aug 15, 2026

Categories

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

LangChain integrationLangChain

Source: README excerpt (regex_v1, Aug 15, 2026)

1. Clone repo: `git clone https://github.com/btrcm00/chatbot-with-langchain.git`
Source link
Node.js runtimeNode.js

Source: README excerpt (regex_v1, Aug 15, 2026)

2. Install packages: `npm install`
Source link
OpenAI APIOpenAI API

Source: README excerpt (regex_v1, Aug 15, 2026)

# Edit the .env file to add your OpenAI API key
Source link

Tags

README

Quick Start with Setup Script

The easiest way to run the entire application is using our setup script:

  1. Make the script executable:

    chmod +x setup.sh
    
  2. Run the setup script:

    ./setup.sh
    
  3. Follow the prompts:

    • The script will create necessary .env files if they don't exist
    • Choose between Docker Compose deployment or local development
    • The script will guide you through the rest of the setup process

For Windows users, use setup.bat instead.


Quick Start with Docker Compose Manually

If you prefer to run the commands manually:

  1. Set up environment variables:

    # For backend
    cp backend/.env.example backend/.env
    # Edit the .env file to add your OpenAI API key
    
    # For frontend
    cp frontend/.env.example frontend/.env
    
  2. Start the application:

    docker-compose up -d
    
  3. Access the application:

  4. Stop the application:

    docker-compose down
    

Running Locally (without Docker)

  1. Download the models for the languages to use in anonymizer. PII support.
    1. python -m spacy download en_core_web_md
  2. 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 or set up OpenAI API key
    3. cd backend
    4. Install required packages: pip install -r requirements.txt
    5. Create MongoDB database and config environment variables to connect Mongo
    6. Run: python -m uvicorn backend.main:app --reload --host 0.0.0.0 --port 8080
  3. RUN frontend
    1. cd frontend
    2. Install packages: npm install
    3. Start frontend: npm run dev

For agents

This page has a .md twin and JSON over the API.

Was this helpful?

Anonymous feedback helps us improve pages and translations.