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Langchain Chatbi

Langchain Chatbi is a Python application for conversational business intelligence that uses a language model, metric matching and a local vector store to help users find and understand data knowledge and analyze results through natural-language dialogue.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Langchain Chatbi — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when evaluating or prototyping conversational BI with local model deployment or documented HTTP model calls, and when matching user questions to metrics is part of the workflow.

Who it is for

  • BI practitioners exploring natural-language access to business metrics.
  • Developers prototyping a self-hosted conversational analytics application.

Documented uses

  • Ask BI questions in natural language and identify relevant metrics.
  • Find and understand data knowledge with a vector-backed assistant.
  • Explore an implementation that combines a language model with a Gradio interface.
Before you choose

Requirements & limits

The public GitHub repository provides clone, dependency-installation and local launch instructions.

  • The documented environment requires Python 3.8–3.11.
  • The README describes local ChatGLM deployment and HTTP calls to Baichuan or Qwen.
  • The setup examples use ModelScope to download embedding and language models.
  • The README documents starting a Gradio interface but does not describe a full BI data-source connection workflow.

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

Set up and launch the Gradio interface

Prepare the documented Python environment, download the named models and start the project's Gradio web interface.

Before you start

  • Python 3.8–3.11
  • The project repository and its requirements.txt dependencies
  • ModelScope access to download the documented embedding and language models

Steps

  1. Clone the repository and change into the Langchain-ChatBI directory.

  2. Install dependencies with pip3 install -r requirements.txt.

  3. Use ModelScope snapshot_download to download an embedding model, such as AI-ModelScope/bge-large-en-v1.5 or Jerry0/text2vec-large-chinese, and the ZhipuAI/chatglm2-6b-int4 language model.

  4. Run python3 main_webui.py to start the Gradio interface.

Expected outcome

The documented setup path starts the Gradio web interface for the application.

Guide limitations

  • The README provides a local ChatGLM model download example; it also describes HTTP calls to Baichuan and Qwen but does not provide their configuration steps in this setup procedure.
  • The instructions do not document connecting a BI data source or completing a production deployment.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
dynamiclu/Langchain-ChatBI · Official homepage title · retained in its source language
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