text_embedding_qdrant

Description

Example to build a vector index in Qdrant based on local files.

Pre-requisites

• Install Postgres if you don't have one.

• Run Qdrant.

docker run -d -p 6334:6334 -p 6333:6333 qdrant/qdrant

• Create a collection to export the embeddings to.

curl  -X PUT \
  'http://localhost:6333/collections/cocoindex' \
  --header 'Content-Type: application/json' \
  --data-raw '{
  "vectors": {
    "text_embedding": {
      "size": 384,
      "distance": "Cosine"
    }
  }
}'

You can view the collections and data with the Qdrant dashboard at http://localhost:6333/dashboard.

Run

Install dependencies:

pip install -e .

Setup:

python main.py cocoindex setup

Update index:

python main.py cocoindex update

Run:

python main.py

CocoInsight

CocoInsight is in Early Access now (Free) 😊 You found us! A quick 3 minute video tutorial about CocoInsight: Watch on YouTube.

Run CocoInsight to understand your RAG data pipeline:

python main.py cocoindex server -c https://cocoindex.io

Then open the CocoInsight UI at https://cocoindex.io/cocoinsight.