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CanaR

CanaR is a chat application that lets you create and expose RAG chatbots (Retrieval-Augmented Generation) powered by LLMs. It provides a web interface to chat with assistants that can rely on a document knowledge base (e.g., internal documentation, HR or legal documents, publications, code, …) previously ingested into Qdrant — typically via the AgoRa ingestion tool.

CanaR lets you:

• 🗨️ Access a chat interface with user account management
• 🤖 Define multiple chatbots/assistants, each with a custom prompt and behavior tailored to specific tasks
• 🔎 Augment answers with document retrieval (RAG) and cite the sources used
• 📄 Keep conversation history (sessions and messages)

For more information about installation, features, and contributing, see the documentation: CanaR Documentation (under construction 🚧 ).

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Quickstart (local)

A minimal Makefile is available (optional):

Targets:
  make up            - Start Qdrant + Postgres (docker compose)
  make down          - Stop containers
  make reset         - Stop + remove volumes
  make logs          - Follow docker logs
  make venv          - Create venv (.venv)
  make install       - Install CanaR (editable) + dev tools
  make run           - Run CanaR (entrypoint or Streamlit fallback)
  make test          - Run tests (pytest)
  make lint          - Run ruff lint (check)
  make format        - Auto-format with ruff
  make format-check  - Check formatting with ruff
  make ci            - Run lint + format-check + tests

1) Start Qdrant + Postgres (Docker Compose)

cd infra
docker compose up -d
docker compose ps

or make up

The Qdrant UI is available at http://localhost:6333/dashboard.

2) Configure environment variables

Create a .env file at the root of the project from .env.example.

⚠️ Important: the value of QDRANT_URL and DB_POSTGRES_URL depends on where CanaR is running:

• CanaR started on the host (venv / canar / streamlit run) → use localhost
• CanaR started in Docker (canar service in a compose) → use Docker service names: qdrant, postgres

Variable	Description	Example
LLM_API_BASE	LLM API base URL	https://api.mistral.ai/v1
LLM_API_KEY	LLM API key	sk-...
LLM_MODEL	Model name	mistral-medium
EMBED_API_BASE	Embeddings API base URL	https://api.mistral.ai/v1
EMBED_API_KEY	Embeddings API key	sk-...
EMBED_MODEL	Embeddings model name	mistral-embed
QDRANT_URL	Qdrant URL	http://localhost:6333 (host) or http://qdrant:6333 (docker)
QDRANT_API_KEY	Qdrant API key (if enabled)	...
QDRANT_COLLECTIONS	Allowed collections (comma-separated)	col1,col2
DB_POSTGRES_URL	Postgres URL (SQLAlchemy/psycopg)	postgresql+psycopg://canar:canar@localhost:5432/canar (host) or ...@postgres:5432/... (docker)

3) Install and run CanaR

python -m venv .venv
source .venv/bin/activate

pip install -U pip
pip install -e .

or make venv & make install

Run the application:

canar

or make run

If the canar command is not available, run Streamlit directly:

streamlit run canar/app/main.py --server.headless true --server.port 8501

Requirements

• Docker + Docker Compose (Linux) or Docker Desktop (Windows)
• Python ≥ 3.10

CanaR requires:

• a vector database (RAG): Qdrant
• a relational database (user accounts + history): Postgres

The docker-compose file is provided in infra/docker-compose.yml.

Configuration

Example .env:

# LLM (example)
LLM_API_BASE=https://url_llm/v1
LLM_API_KEY=
LLM_MODEL=model_name

# Embeddings (example)
EMBED_API_BASE=https://url_embed/v1
EMBED_API_KEY=
EMBED_MODEL=model_name

# Qdrant
# QDRANT_URL=http://qdrant:6333        # if CanaR runs in Docker
QDRANT_URL=http://localhost:6333       # if CanaR runs on the host
QDRANT_API_KEY=
QDRANT_COLLECTIONS=collection1_v1,collection2_v1

# Postgres
# DB_POSTGRES_URL=postgresql+psycopg://canar:canar@postgres:5432/canar   # docker
DB_POSTGRES_URL=postgresql+psycopg://canar:canar@localhost:5432/canar    # host

Contributing

See: CONTRIBUTING.md

License

TBD (to be confirmed by maintainers).

Common issues

Ports

The following ports must be available:

Service	Default port
Postgres	5432
Qdrant	6333
CanaR (Streamlit)	8501