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RAG & Knowledge Systems

Chatbot

Chatbot is a Python workshop project that demonstrates a retrieval-augmented chatbot for answering questions about OceanBase documentation, combining document embeddings, OceanBase vector retrieval, a Tongyi Qianwen language model and a Streamlit chat interface.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Chatbot — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it as a reference implementation when exploring a documentation Q&A workflow built around OceanBase retrieval and a language model.

Who it is for

  • Developers learning how to build a RAG chatbot over technical documentation.
  • Teams prototyping documentation question answering with OceanBase vector storage.

Documented uses

  • Convert documentation into embeddings and store them with structured data in OceanBase.
  • Retrieve relevant documentation passages for a user question.
  • Generate and stream an answer using retrieved passages as context.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. The documented quick start launches the interface locally with Docker Compose.

  • The example uses OceanBase 4.3.5 or above for vector and structured-data storage.
  • The documented components include Tongyi Qianwen embeddings and language-model capabilities, with Streamlit as the interface.
  • The manual setup requires Python 3.11 or later, pip, uv and database connection configuration.
  • Embedding configuration must use a vector dimension matching the selected embedding model.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Start the chatbot with Docker Compose

Configure the documented quick-start environment and launch the chatbot services.

Before you start

  • Docker Compose
  • A project checkout
  • An API key entered in the environment file

Steps

  1. Change to the project's docker directory.

  2. Copy .env.example to .env.

  3. Set API_KEY in .env.

  4. Run docker compose up -d.

  5. Open http://localhost:8501.

Expected outcome

The documented local web interface is available at http://localhost:8501.

Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
ChatbotConvert documentation into embeddings and store them with structured data in OceanBase.Self-hosted, ServerThe repository is public and MIT-licensed. The documented quick start launches the interface locally with Docker Compose.
Rag Powered AI Assistant Transforming Data Retrieval And Analysis Across The Web And PdfsRetrieve relevant passages from web and PDF content to answer a question.Web
LangChain in Supabase Edge FunctionsInitialize and start a Supabase project locally.Cloud, Server
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
ob-labs/ChatBot · Official homepage title · retained in its source language
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