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¶

You need to setup your own VectorDB service (Redis in this example), and ingest your knowledge documents into the vector database.

As for Redis, you could start a docker container using the following commands. Remember to ingest data into it manually.

docker run -d --name="redis-vector-db" -p 6379:6379 -p 8001:8001 redis/redis-stack:7.2.0-v9

1.5 Start Retriever Service¶

export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
python retriever_redis.py

🚀2. Start Microservice with Docker (Option 2)¶

2.1 Setup Environment Variables¶

export RETRIEVE_MODEL_ID="BAAI/bge-base-en-v1.5"
export REDIS_URL="redis://${your_ip}:6379"
export INDEX_NAME=${your_index_name}
export TEI_EMBEDDING_ENDPOINT="http://${your_ip}:6060"
export HUGGINGFACEHUB_API_TOKEN=${your_hf_token}

2.2 Build Docker Image¶

cd ../../../../
docker build -t opea/retriever-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/redis/langchain/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-redis-server" -p 7000:7000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN opea/retriever-redis:latest

2.4 Run Docker with Docker Compose (Option B)¶

docker compose -f docker_compose_retriever.yaml up -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'