Quick Chatbot

Internal Q&A

A Streamlit chatbot over an embedded Chroma store — no separate database server, no Node toolchain. The fastest way to stand up an internal Q&A bot for a small team.

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Live previewinteractive mock — no backend

localhost:8501Streamlit

Internal Q&A

Team brain.

Try one — live mock, no backend.

Stack

Frontend

Streamlit

Architecture

Standard RAG

Framework

raw python

Vector DB

chroma

Corpus

small

Complexity

Low

How it works

Browser→

Streamlit app→

FastAPI /query→

Standard RAG

The frontend talks only to the backend; your API keys + pipeline URL stay server-side.

Get started

Option A · Docker Compose (one command)

cp .env.example .env        # add your API key(s)
docker compose up --build

Option B · Backend (manual)

cd backend
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env        # add your API key(s)
python pipeline.py ingest ./corpus
uvicorn serve:app --reload  # http://localhost:8000

Option B · Frontend (Streamlit)

cd frontend
pip install -r requirements.txt
cp .env.example .env
streamlit run app.py        # http://localhost:8501

The downloaded README.md has the full guide — vector DB setup, API keys, and deployment to Render/Railway + Streamlit Cloud.

Project structure28 backend · 6 frontend · 38 files total

backend/

.github/

workflows/

deploy.yml

eval/

__init__.py

README.md

run_eval.sh

synthetic_qa.py

test_answer_quality.py

test_retrieval.py

generation/

__init__.py

generator.py

prompt.py

ingestion/

__init__.py

chunker.py

embedder.py

loader.py

observability/

__init__.py

tracing.py

retrieval/

__init__.py

reranker.py

retriever.py

.env.example

config.yaml

docker-compose.yml

Dockerfile

pipeline.py

README.md

render.yaml

requirements.txt

serve.py

frontend/

.streamlit/

config.toml

.env.example

.gitignore

app.py

Dockerfile

requirements.txt

.env.example

.gitignore

docker-compose.yml

README.md

Ready to build?

Download the full monorepo and follow the README.

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