Edge Craft Retrieval-Augmented Generation¶

Edge Craft RAG (EC-RAG) is a customizable, tunable and production-ready Retrieval-Augmented Generation system for edge solutions. It is designed to curate the RAG pipeline to meet hardware requirements at edge with guaranteed quality and performance.

What’s New in this release?¶

Chat history support for multi-session chatqna
Knowledge base support for EC-RAG
Multi-Arc support for multiple LLM inference serving engine

Quick Start Guide¶

(Optional) Build Docker Images for Mega Service, Server and UI by your own¶

All the docker images can be automatically‌ pulled, If you want to build the images by your own, please follow the steps:

cd GenAIExamples/EdgeCraftRAG

docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy --build-arg no_proxy=$no_proxy -t opea/edgecraftrag:latest -f Dockerfile .
docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy --build-arg no_proxy=$no_proxy -t opea/edgecraftrag-server:latest -f Dockerfile.server .
docker build --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy --build-arg no_proxy=$no_proxy -t opea/edgecraftrag-ui:latest -f ui/docker/Dockerfile.ui .

Using Intel Arc GPU¶

Local inference with OpenVINO for Intel Arc GPU¶

You can select “local” type in generation field which is the default approach to enable Intel Arc GPU for LLM. You don’t need to build images for “local” type.

vLLM with OpenVINO for Intel Arc GPU¶

You can also select “vLLM” as generation type, to enable this type, you’ll need to build the vLLM image for Intel Arc GPU before service bootstrap. Please follow this link vLLM with OpenVINO to build the vLLM image.

Start Edge Craft RAG Services with Docker Compose¶

cd GenAIExamples/EdgeCraftRAG/docker_compose/intel/gpu/arc

export MODEL_PATH="your model path for all your models"
export DOC_PATH="your doc path for uploading a dir of files"
export UI_TMPFILE_PATH="your UI cache path for transferring files"
# If you have a specific prompt template, please uncomment the following line
# export PROMPT_PATH="your prompt path for prompt templates"

# Make sure all 3 folders have 1000:1000 permission, otherwise
# chown 1000:1000 ${MODEL_PATH} ${DOC_PATH} ${UI_TMPFILE_PATH}
# In addition, also make sure the .cache folder has 1000:1000 permission, otherwise
# chown 1000:1000 $HOME/.cache

# Use `ip a` to check your active ip
export HOST_IP="your host ip"

# Check group id of video and render
export VIDEOGROUPID=$(getent group video | cut -d: -f3)
export RENDERGROUPID=$(getent group render | cut -d: -f3)

# If you have a proxy configured, uncomment below line
# export no_proxy=${no_proxy},${HOST_IP},edgecraftrag,edgecraftrag-server
# export NO_PROXY=${NO_PROXY},${HOST_IP},edgecraftrag,edgecraftrag-server
# If you have a HF mirror configured, it will be imported to the container
# export HF_ENDPOINT="your HF mirror endpoint"

# By default, the ports of the containers are set, uncomment if you want to change
# export MEGA_SERVICE_PORT=16011
# export PIPELINE_SERVICE_PORT=16011
# export UI_SERVICE_PORT="8082"

# Prepare models for embedding, reranking and generation, you can also choose other OpenVINO optimized models
# Here is the example:
pip install --upgrade --upgrade-strategy eager "optimum[openvino]"

# If below optimum-cli commands show errors, please set transformers==4.49.0 to fix: pip install transformers==4.49.0
optimum-cli export openvino -m BAAI/bge-small-en-v1.5 ${MODEL_PATH}/BAAI/bge-small-en-v1.5 --task sentence-similarity
optimum-cli export openvino -m BAAI/bge-reranker-large ${MODEL_PATH}/BAAI/bge-reranker-large --task text-classification
optimum-cli export openvino -m Qwen/Qwen2-7B-Instruct ${MODEL_PATH}/Qwen/Qwen2-7B-Instruct/INT4_compressed_weights --weight-format int4

Launch services with local inference¶

docker compose -f compose.yaml up -d

Launch services with vLLM + OpenVINO inference service¶

Set up Additional Environment Variables and start with compose_vllm.yaml

export LLM_MODEL=#your model id
export VLLM_SERVICE_PORT=8008
export vLLM_ENDPOINT="http://${HOST_IP}:${VLLM_SERVICE_PORT}"
export HF_TOKEN=#your HF token

docker compose -f compose_vllm.yaml up -d

Launch services with vLLM for multi Intel Arc GPUs inference service¶

The docker file can be pulled automatically‌, you can also pull the image manually:

docker pull intelanalytics/ipex-llm-serving-xpu:0.8.3-b18

Generate your nginx config file

export HOST_IP=#your host ip
export NGINX_PORT=8086 #set port for nginx
# If you are running with 1 vllm container:
export NGINX_PORT_0=8100 # you can change the port to your preferrance
export NGINX_PORT_1=8100 # you can change the port to your preferrance
# If you are running with 2 vllm containers:
export NGINX_PORT_0=8100 # you can change the port to your preferrance
export NGINX_PORT_1=8200 # you can change the port to your preferrance
# Generate your nginx config file
envsubst < GenAIExamples/EdgeCraftRAG/nginx/nginx.conf.template > <your_nginx_config_path>/nginx.conf
# set NGINX_CONFIG_PATH
export NGINX_CONFIG_PATH="<your_nginx_config_path>/nginx.conf"

Set up Additional Environment Variables and start with compose_vllm_multi-arc.yaml

# For 1 vLLM container(1 DP) with  multi Intel Arc GPUs
export vLLM_ENDPOINT="http://${HOST_IP}:${NGINX_PORT}"
export LLM_MODEL_PATH=#your model path
export LLM_MODEL=#your model id
export CONTAINER_COUNT="single_container"
export TENSOR_PARALLEL_SIZE=#your Intel Arc GPU number to do inference
export SELECTED_XPU_0=<which GPU to select to run> # example for selecting 2 Arc GPUs: SELECTED_XPU_0=0,1

# For 2 vLLM container(2 DP) with  multi Intel Arc GPUs
export vLLM_ENDPOINT="http://${HOST_IP}:${NGINX_PORT}"
export LLM_MODEL_PATH=#your model path
export LLM_MODEL=#your model id
export CONTAINER_COUNT="multi_container"
export TENSOR_PARALLEL_SIZE=#your Intel Arc GPU number to do inference
export SELECTED_XPU_0=<which GPU to select to run for container 0>
export SELECTED_XPU_1=<which GPU to select to run for container 1>

# Below are the extra env you can set for vllm
export MAX_NUM_SEQS=<MAX_NUM_SEQS value>
export MAX_NUM_BATCHED_TOKENS=<MAX_NUM_BATCHED_TOKENS value>
export MAX_MODEL_LEN=<MAX_MODEL_LEN value>
export LOAD_IN_LOW_BIT=<the weight type value> # expected: sym_int4, asym_int4, sym_int5, asym_int5 or sym_int8
export CCL_DG2_USM=<CCL_DG2_USM value> # Needed on Core to enable USM (Shared Memory GPUDirect). Xeon supports P2P and doesn't need this.

start with compose_vllm_multi-arc.yaml

docker compose -f docker_compose/intel/gpu/arc/compose_vllm_multi-arc.yaml --profile ${CONTAINER_COUNT} up -d

ChatQnA with LLM Example (Command Line)¶

cd GenAIExamples/EdgeCraftRAG

# Activate pipeline test_pipeline_local_llm
curl -X POST http://${HOST_IP}:16010/v1/settings/pipelines -H "Content-Type: application/json" -d @tests/test_pipeline_local_llm.json | jq '.'

# Will need to wait for several minutes
# Expected output:
# {
#   "idx": "3214cf25-8dff-46e6-b7d1-1811f237cf8c",
#   "name": "rag_test",
#   "comp_type": "pipeline",
#   "node_parser": {
#     "idx": "ababed12-c192-4cbb-b27e-e49c76a751ca",
#     "parser_type": "simple",
#     "chunk_size": 400,
#     "chunk_overlap": 48
#   },
#   "indexer": {
#     "idx": "46969b63-8a32-4142-874d-d5c86ee9e228",
#     "indexer_type": "faiss_vector",
#     "model": {
#       "idx": "7aae57c0-13a4-4a15-aecb-46c2ec8fe738",
#       "type": "embedding",
#       "model_id": "BAAI/bge-small-en-v1.5",
#       "model_path": "/home/user/models/bge_ov_embedding",
#       "device": "auto"
#     }
#   },
#   "retriever": {
#     "idx": "3747fa59-ff9b-49b6-a8e8-03cdf8c979a4",
#     "retriever_type": "vectorsimilarity",
#     "retrieve_topk": 30
#   },
#   "postprocessor": [
#     {
#       "idx": "d46a6cae-ba7a-412e-85b7-d334f175efaa",
#       "postprocessor_type": "reranker",
#       "model": {
#         "idx": "374e7471-bd7d-41d0-b69d-a749a052b4b0",
#         "type": "reranker",
#         "model_id": "BAAI/bge-reranker-large",
#         "model_path": "/home/user/models/bge_ov_reranker",
#         "device": "auto"
#       },
#       "top_n": 2
#     }
#   ],
#   "generator": {
#     "idx": "52d8f112-6290-4dd3-bc28-f9bd5deeb7c8",
#     "generator_type": "local",
#     "model": {
#       "idx": "fa0c11e1-46d1-4df8-a6d8-48cf6b99eff3",
#       "type": "llm",
#       "model_id": "qwen2-7b-instruct",
#       "model_path": "/home/user/models/qwen2-7b-instruct/INT4_compressed_weights",
#       "device": "auto"
#     }
#   },
#   "status": {
#     "active": true
#   }
# }

# Prepare data from local directory
curl -X POST http://${HOST_IP}:16010/v1/data -H "Content-Type: application/json" -d '{"local_path":"docs/#REPLACE WITH YOUR DIR WITHIN MOUNTED DOC PATH#"}' | jq '.'

# Validate Mega Service
curl -X POST http://${HOST_IP}:16011/v1/chatqna -H "Content-Type: application/json" -d '{"messages":"#REPLACE WITH YOUR QUESTION HERE#", "top_n":5, "max_tokens":512}' | jq '.'

ChatQnA with LLM Example (UI)¶

Open your browser, access http://${HOST_IP}:8082

Your browser should be running on the same host of your console, otherwise you will need to access UI with your host domain name instead of ${HOST_IP}.

To create a default pipeline, you need to click the Create Pipeline button on the RAG Settings page. You can also create multiple pipelines or update existing pipelines through the Pipeline Configuration, but please note that active pipelines cannot be updated. create_pipeline

After the pipeline creation, you can upload your data in the Chatbot page. upload_data

Then, you can submit messages in the chat box. chat_with_rag

If you want to try Gradio UI, please launch service through compose_gradio.yaml, then access http://${HOST_IP}:8082 on your browser:

docker compose -f compose_gradio.yaml up -d

Advanced User Guide¶

Pipeline Management¶

Create a pipeline¶

curl -X POST http://${HOST_IP}:16010/v1/settings/pipelines -H "Content-Type: application/json" -d @tests/test_pipeline_local_llm.json | jq '.'

Update a pipeline¶

curl -X PATCH http://${HOST_IP}:16010/v1/settings/pipelines/rag_test_local_llm  -H "Content-Type: application/json" -d @tests/test_pipeline_local_llm.json | jq '.'

Check all pipelines¶

curl -X GET http://${HOST_IP}:16010/v1/settings/pipelines -H "Content-Type: application/json" | jq '.'

Activate a pipeline¶

curl -X PATCH http://${HOST_IP}:16010/v1/settings/pipelines/rag_test_local_llm -H "Content-Type: application/json" -d '{"active": "true"}' | jq '.'

Remove a pipeline¶

# Firstly, deactivate the pipeline if the pipeline status is active
curl -X PATCH http://${HOST_IP}:16010/v1/settings/pipelines/rag_test_local_llm -H "Content-Type: application/json" -d '{"active": "false"}' | jq '.'
# Then delete the pipeline
curl -X DELETE http://${HOST_IP}:16010/v1/settings/pipelines/rag_test_local_llm -H "Content-Type: application/json" | jq '.'

Get pipeline json¶

curl -X GET http://${HOST_IP}:16010/v1/settings/pipelines/{name}/json -H "Content-Type: application/json" | jq '.'

Import pipeline from a json file¶

curl -X POST http://${HOST_IP}:16010/v1/settings/pipelines/import -H "Content-Type: multipart/form-data" -F "file=@your_test_pipeline_json_file.txt"| jq '.'

Enable and check benchmark for pipelines¶

⚠️ NOTICE ⚠️¶

Benchmarking activities may significantly reduce system performance.

DO NOT perform benchmarking in a production environment.

# Set ENABLE_BENCHMARK as true before launch services
export ENABLE_BENCHMARK="true"

# check the benchmark data for pipeline {pipeline_name}
curl -X GET http://${HOST_IP}:16010/v1/settings/pipelines/{pipeline_name}/benchmark -H "Content-Type: application/json" | jq '.'

Model Management¶

Load a model¶

curl -X POST http://${HOST_IP}:16010/v1/settings/models -H "Content-Type: application/json" -d '{"model_type": "reranker", "model_id": "BAAI/bge-reranker-large", "model_path": "./models/bge_ov_reranker", "device": "cpu", "weight": "INT4"}' | jq '.'

It will take some time to load the model.

Check all models¶

curl -X GET http://${HOST_IP}:16010/v1/settings/models -H "Content-Type: application/json" | jq '.'

Update a model¶

curl -X PATCH http://${HOST_IP}:16010/v1/settings/models/BAAI/bge-reranker-large -H "Content-Type: application/json" -d '{"model_type": "reranker", "model_id": "BAAI/bge-reranker-large", "model_path": "./models/bge_ov_reranker", "device": "gpu", "weight": "INT4"}' | jq '.'

Check a certain model¶

curl -X GET http://${HOST_IP}:16010/v1/settings/models/BAAI/bge-reranker-large -H "Content-Type: application/json" | jq '.'

Delete a model¶

curl -X DELETE http://${HOST_IP}:16010/v1/settings/models/BAAI/bge-reranker-large -H "Content-Type: application/json" | jq '.'

File Management¶

Add a text¶

curl -X POST http://${HOST_IP}:16010/v1/data -H "Content-Type: application/json" -d '{"text":"#REPLACE WITH YOUR TEXT"}' | jq '.'

Add files from existed file path¶

curl -X POST http://${HOST_IP}:16010/v1/data -H "Content-Type: application/json" -d '{"local_path":"docs/#REPLACE WITH YOUR DIR WITHIN MOUNTED DOC PATH#"}' | jq '.'
curl -X POST http://${HOST_IP}:16010/v1/data -H "Content-Type: application/json" -d '{"local_path":"docs/#REPLACE WITH YOUR FILE WITHIN MOUNTED DOC PATH#"}' | jq '.'

Check all files¶

curl -X GET http://${HOST_IP}:16010/v1/data/files -H "Content-Type: application/json" | jq '.'

Check one file¶

curl -X GET http://${HOST_IP}:16010/v1/data/files/test2.docx -H "Content-Type: application/json" | jq '.'

Delete a file¶

curl -X DELETE http://${HOST_IP}:16010/v1/data/files/test2.docx -H "Content-Type: application/json" | jq '.'

Update a file¶

curl -X PATCH http://${HOST_IP}:16010/v1/data/files/test.pdf -H "Content-Type: application/json" -d '{"local_path":"docs/#REPLACE WITH YOUR FILE WITHIN MOUNTED DOC PATH#"}' | jq '.'

System Prompt Management¶

Get system prompt¶

curl -X GET http://${HOST_IP}:16010/v1/chatqna/prompt -H "Content-Type: application/json" | jq '.'

Update system prompt¶

curl -X POST http://${HOST_IP}:16010/v1/chatqna/prompt -H "Content-Type: application/json" -d '{"prompt":"This is a template prompt"}' | jq '.'

Reset system prompt¶

curl -X POST http://${HOST_IP}:16010/v1/chatqna/prompt/reset -H "Content-Type: application/json" | jq '.'

Use custom system prompt file¶

curl -X POST http://${HOST_IP}:16010/v1/chatqna/prompt-file -H "Content-Type: multipart/form-data" -F "file=@your_prompt_file.txt"