DocRetriever Application with Docker

DocRetriever are the most widely adopted use case for leveraging the different methodologies to match user query against a set of free-text records. DocRetriever is essential to RAG system, which bridges the knowledge gap by dynamically fetching relevant information from external sources, ensuring that responses generated remain factual and current. The core of this architecture are vector databases, which are instrumental in enabling efficient and semantic retrieval of information. These databases store data as vectors, allowing RAG to swiftly access the most pertinent documents or data points based on semantic similarity.

1. Build Images for necessary microservices. (Optional after docker image release)

• Embedding TEI Image

  git clone https://github.com/opea-project/GenAIComps.git
  cd GenAIComps
  docker build -t opea/embedding-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/tei/langchain/Dockerfile .
  

• Retriever Vector store Image

  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 .
  

• Rerank TEI Image

  docker build -t opea/reranking-tei:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/reranks/tei/Dockerfile .
  

• Dataprep Image

  docker build -t opea/dataprep-on-ray-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain_ray/Dockerfile .
  

2. Build Images for MegaService

cd ..
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/DocIndexRetriever
docker build --no-cache -t opea/doc-index-retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./Dockerfile .

3. Start all the services Docker Containers

export host_ip="YOUR IP ADDR"
export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-base"
export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006"
export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808"
export TGI_LLM_ENDPOINT="http://${host_ip}:8008"
export REDIS_URL="redis://${host_ip}:6379"
export INDEX_NAME="rag-redis"
export MEGA_SERVICE_HOST_IP=${host_ip}
export EMBEDDING_SERVICE_HOST_IP=${host_ip}
export RETRIEVER_SERVICE_HOST_IP=${host_ip}
export RERANK_SERVICE_HOST_IP=${host_ip}
export LLM_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8000/v1/retrievaltool"
export DATAPREP_SERVICE_ENDPOINT="http://${host_ip}:6007/v1/dataprep"
cd GenAIExamples/DocIndexRetriever/intel/cpu/xoen/
docker compose up -d

4. Validation

Add Knowledge Base via HTTP Links:

curl -X POST "http://${host_ip}:6007/v1/dataprep" \
     -H "Content-Type: multipart/form-data" \
     -F 'link_list=["https://opea.dev"]'

# expected output
{"status":200,"message":"Data preparation succeeded"}

Retrieval from KnowledgeBase

curl http://${host_ip}:8889/v1/retrievaltool -X POST -H "Content-Type: application/json" -d '{
     "messages": "Explain the OPEA project?"
     }'

# expected output
{"id":"354e62c703caac8c547b3061433ec5e8","reranked_docs":[{"id":"06d5a5cefc06cf9a9e0b5fa74a9f233c","text":"Close SearchsearchMenu WikiNewsCommunity Daysx-twitter linkedin github searchStreamlining implementation of enterprise-grade Generative AIEfficiently integrate secure, performant, and cost-effective Generative AI workflows into business value.TODAYOPEA..."}],"initial_query":"Explain the OPEA project?"}

Note: messages is the required field. You can also pass in parameters for the retriever and reranker in the request. The parameters that can changed are listed below.

1. retriever
* search_type: str = "similarity"
* k: int = 4
* distance_threshold: Optional[float] = None
* fetch_k: int = 20
* lambda_mult: float = 0.5
* score_threshold: float = 0.2

2. reranker
* top_n: int = 1

5. Trouble shooting

1. check all containers are alive

   # redis vector store
   docker container logs redis-vector-db
   # dataprep to redis microservice, input document files
   docker container logs dataprep-redis-server
   
   # embedding microservice
   curl http://${host_ip}:6000/v1/embeddings \
     -X POST \
     -d '{"text":"Explain the OPEA project"}' \
     -H 'Content-Type: application/json' > query
   docker container logs embedding-tei-server
   
   # if you used tei-gaudi
   docker container logs tei-embedding-gaudi-server
   
   # retriever microservice, input embedding output docs
   curl http://${host_ip}:7000/v1/retrieval \
     -X POST \
     -d @query \
     -H 'Content-Type: application/json' > rerank_query
   docker container logs retriever-redis-server
   
   
   # reranking microservice
   curl http://${host_ip}:8000/v1/reranking \
     -X POST \
     -d @rerank_query \
     -H 'Content-Type: application/json' > output
   docker container logs reranking-tei-server
   
   # megaservice gateway
   docker container logs doc-index-retriever-server