Create Local RAG Pipelines with R2R and Ollama for Free

 This video installs R2R (Rag to Riches) with Ollama and local models which is the ultimate open-source framework for building and deploying high-quality Retrieval-Augmented Generation (RAG) systems.

Code:

conda create -n r2r python=3.11 -y && conda activate r2r

pip install -U 'r2r[all]'

pip install -U 'r2r[local-embedding]'

sudo apt install -y postgresql-common

sudo /usr/share/postgresql-common/pgdg/apt.postgresql.org.sh

sudo apt install postgresql-15-pgvector

cd /tmp

sudo -u postgres psql

###create role, database and extension in video

\q to exit

sudo systemctl enable postgresql

sudo service postgresql start

sudo -u postgres psql

export POSTGRES_USER=your_user

export POSTGRES_PASSWORD=your_password

export POSTGRES_HOST=your_host

export POSTGRES_PORT=your_port

export POSTGRES_DBNAME=your_db

mkdir r2r

cd r2r

touch local_ollama

-- and then pasted below config in local_ollama file:

{

  "embedding": {

    "provider": "sentence-transformers",

    "base_model": "all-MiniLM-L6-v2",

    "base_dimension": 384,

    "batch_size": 32

  },

  "eval": {

    "provider": "local",

    "frequency": 0.0,

    "llm":{

      "provider": "litellm"

    }

  },

  "ingestion":{

    "excluded_parsers": {

      "gif": "default",

      "jpeg": "default",

      "jpg": "default",

      "png": "default",

      "svg": "default",

      "mp3": "default",

      "mp4": "default"

    }

  }

}

python3 -m r2r.examples.quickstart ingest_as_files --no-media=true --config_name=local_ollama