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README_opensearch.md

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Retriever Microservice

This retriever microservice is a highly efficient search service designed for handling and retrieving embedding vectors. It operates by receiving an embedding vector as input and conducting a similarity search against vectors stored in a VectorDB database. Users must specify the VectorDB's URL and the index name, and the service searches within that index to find documents with the highest similarity to the input vector.

The service primarily utilizes similarity measures in vector space to rapidly retrieve contentually similar documents. The vector-based retrieval approach is particularly suited for handling large datasets, offering fast and accurate search results that significantly enhance the efficiency and quality of information retrieval.

Overall, this microservice provides robust backend support for applications requiring efficient similarity searches, playing a vital role in scenarios such as recommendation systems, information retrieval, or any other context where precise measurement of document similarity is crucial.

🚀1. Start Microservice with Python (Option 1)

To start the retriever microservice, you must first install the required python packages.

1.1 Install Requirements

pip install -r requirements.txt

1.2 Start TEI Service

model=BAAI/bge-base-en-v1.5
volume=$PWD/data
docker run -d -p 6060:80 -v $volume:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 --model-id $model

1.3 Verify the TEI Service

Health check the embedding service with:

curl 127.0.0.1:6060/embed \
    -X POST \
    -d '{"inputs":"What is Deep Learning?"}' \
    -H 'Content-Type: application/json'

1.4 Setup VectorDB Service

Please refer to this readme.

1.5 Start Retriever Service

export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
export RETRIEVER_COMPONENT_NAME="OPEA_RETRIEVER_OPENSEARCH"
export HUGGINGFACEHUB_API_TOKEN=${your_hf_token}
python opea_retrievers_microservice.py

🚀2. Start Microservice with Docker (Option 2)

2.1 Setup Environment Variables

export RETRIEVE_MODEL_ID="BAAI/bge-base-en-v1.5"
export OPENSEARCH_URL="http://${your_ip}:9200"
export INDEX_NAME=${your_index_name}
export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
export HUGGINGFACEHUB_API_TOKEN=${your_hf_token}
export OPENSEARCH_INITIAL_ADMIN_PASSWORD=${your_opensearch_initial_admin_password}
export RETRIEVER_COMPONENT_NAME="OPEA_RETRIEVER_OPENSEARCH"

2.2 Build Docker Image

cd ../../../
docker build -t opea/retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/src/Dockerfile .

To start a docker container, you have two options:

  • A. Run Docker with CLI
  • B. Run Docker with Docker Compose

You can choose one as needed.

2.3 Run Docker with CLI (Option A)

docker run -d --name="retriever-opensearch-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e OPENSEARCH_URL=$OPENSEARCH_URL -e INDEX_NAME=$INDEX_NAME -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN -e RETRIEVER_COMPONENT_NAME=$RETRIEVER_COMPONENT_NAME opea/retriever:latest

2.4 Run Docker with Docker Compose (Option B)

cd ../deployment/docker_compose
export service_name="retriever-opensearch"
docker compose -f compose.yaml up ${service_name} -d

🚀3. Consume Retriever Service

3.1 Check Service Status

curl http://localhost: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'

You can set the parameters for the retriever.

export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
curl http://localhost:7000/v1/retrieval \
  -X POST \
  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity\", \"k\":4}" \
  -H 'Content-Type: application/json'
export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
curl http://localhost:7000/v1/retrieval \
  -X POST \
  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity_distance_threshold\", \"k\":4, \"distance_threshold\":1.0}" \
  -H 'Content-Type: application/json'
export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
curl http://localhost:7000/v1/retrieval \
  -X POST \
  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"similarity_score_threshold\", \"k\":4, \"score_threshold\":0.2}" \
  -H 'Content-Type: application/json'
export your_embedding=$(python -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
curl http://localhost:7000/v1/retrieval \
  -X POST \
  -d "{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${your_embedding},\"search_type\":\"mmr\", \"k\":4, \"fetch_k\":20, \"lambda_mult\":0.5}" \
  -H 'Content-Type: application/json'