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1.9 KiB
1.9 KiB
Retriever Microservice with Qdrant
1. 🚀Start Microservice with Python (Option 1)
1.1 Install Requirements
pip install -r requirements.txt
1.2 Start Qdrant Server
Please refer to this readme.
1.3 Setup Environment Variables
export QDRANT_HOST=${your_qdrant_host_ip}
export QDRANT_PORT=6333
export EMBED_DIMENSION=${your_embedding_dimension}
export INDEX_NAME=${your_index_name}
1.4 Start Retriever Service
export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
python retriever_qdrant.py
2. 🚀Start Microservice with Docker (Option 2)
2.1 Setup Environment Variables
export QDRANT_HOST=${your_qdrant_host_ip}
export QDRANT_PORT=6333
export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
2.2 Build Docker Image
cd ../../../../
docker build -t opea/retriever-qdrant:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/qdrant/haystack/Dockerfile .
2.3 Run Docker with CLI
docker run -d --name="retriever-qdrant-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e QDRANT_HOST=$QDRANT_HOST -e QDRANT_PORT=$QDRANT_PORT opea/retriever-qdrant:latest
🚀3. Consume Retriever Service
3.1 Check Service Status
curl http://${your_ip}:7000/v1/health_check \
-X GET \
-H 'Content-Type: application/json'
3.2 Consume Embedding Service
To consume the Retriever Microservice, you can generate a mock embedding vector of length 768 with Python.
export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
curl http://${your_ip}:7000/v1/retrieval \
-X POST \
-d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding}}" \
-H 'Content-Type: application/json'