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RAG & Knowledge Systems

Rag Chatbot — jayeshmahapatra

Rag Chatbot — jayeshmahapatra is an MIT-licensed Python application codebase for a retrieval-augmented chatbot that answers questions from technical-blog content. It combines a LangChain backend, a Chroma vector database, API serving with FastAPI and LangServe, and a Next.js and Chakra UI frontend.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Rag Chatbot — jayeshmahapatra — 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 the repository when you want to study or configure a blog-focused RAG chatbot and are prepared to work with its environment files and Docker Compose setup. The original hosted chatbot is no longer live.

Who it is for

  • Developers exploring a retrieval-augmented chatbot for a technical blog.
  • People evaluating a documented web-chatbot stack with a Python backend, vector storage and container setup.

Documented uses

  • Build a chatbot interface that answers questions grounded in blog content.
  • Ingest content into Chroma when the mounted database folders are empty.
  • Examine a RAG implementation using LangChain, FastAPI, LangServe and a Next.js frontend.
Before you choose

Requirements & limits

The repository is public and MIT-licensed. The README states that the original hosted instance is no longer live.

  • Chroma is self-hosted in the described setup rather than using the Weaviate Cloud arrangement of the base project.
  • The documented stack includes Together AI for embeddings and answer generation, with named embedding and generation models.
  • The repository provides separate development and production Docker Compose files, but requires backend, database and frontend environment configuration.
  • The chatbot depends on content being available in its vector database; the README documents an ingestion pipeline for empty mounted folders.

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 the chatbot containers

Prepare the documented development or production container setup.

Before you start

  • A local copy of the repository
  • Docker Compose
  • Backend configuration in dev.config or prod.config
  • chroma/chroma.env and rag_chatbot_backend/keys.env, using their example-file formats
  • rag_chatbot_frontend/.env.local, using the frontend example-file format

Steps

  1. Clone the repository.

  2. Edit the backend configuration for the intended development or production setup.

  3. Create the Chroma, backend keys and frontend environment files from the corresponding examples.

  4. Build and start the containers in detached mode with the development or production Docker Compose file.

Expected outcome

The documented Docker Compose setup builds and starts the selected container configuration.

Guide limitations

  • The README says the original hosted instance is no longer live.
  • The setup instructions do not establish that the containers have been run or verified in another environment.
Documentation 1 ↗
Source-based practical guide

Populate an empty Chroma database

Run the repository’s ingestion pipeline when mounted folders contain no vector database data.

Before you start

  • The chatbot containers are set up and running.
  • The mounted folders have no data.
  • Access to the backend container and its configured environment.

Steps

  1. Use docker ps to find the backend container name or ID.

  2. Open an interactive shell in that container with docker exec -it /bin/bash.

  3. Run python ingestion_pipeline.py.

Expected outcome

The documented ingestion pipeline is run to populate the Chroma vector database.

Guide limitations

  • The README presents this as optional and specifically for empty mounted folders.
  • The documentation does not detail ingestion inputs or confirm a successful run.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

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
Rag Chatbot — jayeshmahapatraBuild a chatbot interface that answers questions grounded in blog content.WebThe repository is public and MIT-licensed. The README states that the original hosted instance is no longer live.
ServietteAnswer questions from a maintained folder of documents and cite the sources used.Linux, Self-hosted, ServerServiette is self-hosted and MIT-licensed. Setup requires a free Pathway license key. Provider API keys are needed for the corresponding API-based embedding or generated-answer options.
Chatbot with PDF for Semantic SearchPrepare document content as embeddings and save it to a supported vector database.WebThe repository is identified as open source and publicly visible on GitHub.
Evidence and transparency

Sources & review

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