Add EdgeCraftRag as a GenAIExample

.github/code_spell_ignore.txt

@@ -0,0 +1,2 @@
+ModelIn
+modelin

EdgeCraftRAG/Dockerfile

@@ -0,0 +1,28 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+FROM python:3.11-slim
+
+SHELL ["/bin/bash", "-o", "pipefail", "-c"]
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+    libgl1-mesa-glx \
+    libjemalloc-dev 
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+COPY ./edgecraftrag /home/user/edgecraftrag
+COPY ./chatqna.py /home/user/chatqna.py
+
+WORKDIR /home/user/edgecraftrag
+RUN pip install --no-cache-dir -r requirements.txt
+
+WORKDIR /home/user
+
+USER user
+
+RUN echo 'ulimit -S -n 999999' >> ~/.bashrc
+
+ENTRYPOINT ["python", "chatqna.py"]

EdgeCraftRAG/Dockerfile.server

@@ -0,0 +1,35 @@
+FROM python:3.11-slim
+
+SHELL ["/bin/bash", "-o", "pipefail", "-c"]
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+    libgl1-mesa-glx \
+    libjemalloc-dev
+
+RUN apt-get update && apt-get install -y gnupg wget 
+RUN wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \
+    gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
+RUN echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu jammy client" | \
+    tee /etc/apt/sources.list.d/intel-gpu-jammy.list
+RUN apt-get update
+RUN apt-get install -y \
+    intel-opencl-icd intel-level-zero-gpu level-zero intel-level-zero-gpu-raytracing \
+    intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
+    libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
+    libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
+    mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo 
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/ 
+
+COPY ./edgecraftrag /home/user/edgecraftrag
+
+WORKDIR /home/user/edgecraftrag
+RUN pip install --no-cache-dir -r requirements.txt
+
+WORKDIR /home/user/
+
+USER user
+
+ENTRYPOINT ["python", "-m", "edgecraftrag.server"]

EdgeCraftRAG/Dockerfile.ui

@@ -0,0 +1,23 @@
+FROM python:3.11-slim
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+    libgl1-mesa-glx \
+    libjemalloc-dev 
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+COPY ./ui /home/user/ui
+COPY ./edgecraftrag /home/user/edgecraftrag
+
+WORKDIR /home/user/edgecraftrag
+RUN pip install --no-cache-dir -r requirements.txt
+
+WORKDIR /home/user/ui
+
+USER user
+
+RUN echo 'ulimit -S -n 999999' >> ~/.bashrc
+
+ENTRYPOINT ["python", "ecragui.py"]

EdgeCraftRAG/README.md

@@ -0,0 +1,274 @@
+# 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.
+
+## Quick Start Guide
+
+### Run Containers with Docker Compose
+
+```bash
+cd GenAIExamples/EdgeCraftRAG/docker_compose/Intel/Arc
+
+export MODEL_PATH="your model path for all your models"
+export DOC_PATH="your doc path for uploading a dir of files"
+export HOST_IP="your host ip"
+export UI_SERVICE_PORT="port for UI service"
+
+# Optional for vllm endpoint
+export vLLM_ENDPOINT="http://${HOST_IP}:8008"
+
+# If you have a proxy configured, uncomment below line
+# export no_proxy=$no_proxy,${HOST_IP},edgecraftrag,edgecraftrag-server
+# If you have a HF mirror configured, it will be imported to the container
+# export HF_ENDPOINT="your HF mirror endpoint"
+
+# By default, the ports of the containers are set, uncomment if you want to change
+# export MEGA_SERVICE_PORT=16011
+# export PIPELINE_SERVICE_PORT=16011
+
+docker compose up -d
+```
+
+### (Optional) Build Docker Images for Mega Service, Server and UI by your own
+
+```bash
+cd GenAIExamples/EdgeCraftRAG
+
+docker build --build-arg http_proxy=$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 Dockerfile.ui .
+```
+
+### ChatQnA with LLM Example (Command Line)
+
+```bash
+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":"#REPLACE WITH YOUR LOCAL DOC DIR#"}' | 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}.
+
+### (Optional) Launch vLLM with OpenVINO service
+
+```bash
+# 1. export LLM_MODEL
+export LLM_MODEL="your model id"
+# 2. Uncomment below code in 'GenAIExamples/EdgeCraftRAG/docker_compose/Intel/Arc/compose.yaml'
+  # vllm-service:
+  #   image: vllm:openvino
+  #   container_name: vllm-openvino-server
+  #   depends_on:
+  #     - vllm-service
+  #   ports:
+  #     - "8008:80"
+  #   environment:
+  #     no_proxy: ${no_proxy}
+  #     http_proxy: ${http_proxy}
+  #     https_proxy: ${https_proxy}
+  #     vLLM_ENDPOINT: ${vLLM_ENDPOINT}
+  #     LLM_MODEL: ${LLM_MODEL}
+  #   entrypoint: /bin/bash -c "\
+  #     cd / && \
+  #     export VLLM_CPU_KVCACHE_SPACE=50 && \
+  #     python3 -m vllm.entrypoints.openai.api_server \
+  #       --model '${LLM_MODEL}' \
+  #       --host 0.0.0.0 \
+  #       --port 80"
+```
+
+## Advanced User Guide
+
+### Pipeline Management
+
+#### Create a pipeline
+
+```bash
+curl -X POST http://${HOST_IP}:16010/v1/settings/pipelines -H "Content-Type: application/json" -d @examples/test_pipeline.json | jq '.'
+```
+
+It will take some time to prepare the embedding model.
+
+#### Upload a text
+
+```bash
+curl -X POST http://${HOST_IP}:16010/v1/data -H "Content-Type: application/json" -d @examples/test_data.json | jq '.'
+```
+
+#### Provide a query to retrieve context with similarity search.
+
+```bash
+curl -X POST http://${HOST_IP}:16010/v1/retrieval -H "Content-Type: application/json" -d @examples/test_query.json | jq '.'
+```
+
+#### Create the second pipeline test2
+
+```bash
+curl -X POST http://${HOST_IP}:16010/v1/settings/pipelines -H "Content-Type: application/json" -d @examples/test_pipeline2.json | jq '.'
+```
+
+#### Check all pipelines
+
+```bash
+curl -X GET http://${HOST_IP}:16010/v1/settings/pipelines -H "Content-Type: application/json" | jq '.'
+```
+
+#### Compare similarity retrieval (test1) and keyword retrieval (test2)
+
+```bash
+# Activate pipeline test1
+curl -X PATCH http://${HOST_IP}:16010/v1/settings/pipelines/test1 -H "Content-Type: application/json" -d '{"active": "true"}' | jq '.'
+# Similarity retrieval
+curl -X POST http://${HOST_IP}:16010/v1/retrieval -H "Content-Type: application/json" -d '{"messages":"number"}' | jq '.'
+
+# Activate pipeline test2
+curl -X PATCH http://${HOST_IP}:16010/v1/settings/pipelines/test2 -H "Content-Type: application/json" -d '{"active": "true"}' | jq '.'
+# Keyword retrieval
+curl -X POST http://${HOST_IP}:16010/v1/retrieval -H "Content-Type: application/json" -d '{"messages":"number"}' | jq '.'
+
+```
+
+### Model Management
+
+#### Load a model
+
+```bash
+curl -X POST http://${HOST_IP}:16010/v1/settings/models -H "Content-Type: application/json" -d @examples/test_model_load.json | jq '.'
+```
+
+It will take some time to load the model.
+
+#### Check all models
+
+```bash
+curl -X GET http://${HOST_IP}:16010/v1/settings/models -H "Content-Type: application/json" | jq '.'
+```
+
+#### Update a model
+
+```bash
+curl -X PATCH http://${HOST_IP}:16010/v1/settings/models/BAAI/bge-reranker-large -H "Content-Type: application/json" -d @examples/test_model_update.json | jq '.'
+```
+
+#### Check a certain model
+
+```bash
+curl -X GET http://${HOST_IP}:16010/v1/settings/models/BAAI/bge-reranker-large -H "Content-Type: application/json" | jq '.'
+```
+
+#### Delete a model
+
+```bash
+curl -X DELETE http://${HOST_IP}:16010/v1/settings/models/BAAI/bge-reranker-large -H "Content-Type: application/json" | jq '.'
+```
+
+### File Management
+
+#### Add a text
+
+```bash
+curl -X POST http://${HOST_IP}:16010/v1/data -H "Content-Type: application/json" -d @examples/test_data.json | jq '.'
+```
+
+#### Add files from existed file path
+
+```bash
+curl -X POST http://${HOST_IP}:16010/v1/data -H "Content-Type: application/json" -d @examples/test_data_dir.json | jq '.'
+curl -X POST http://${HOST_IP}:16010/v1/data -H "Content-Type: application/json" -d @examples/test_data_file.json | jq '.'
+```
+
+#### Check all files
+
+```bash
+curl -X GET http://${HOST_IP}:16010/v1/data/files -H "Content-Type: application/json" | jq '.'
+```
+
+#### Check one file
+
+```bash
+curl -X GET http://${HOST_IP}:16010/v1/data/files/test2.docx -H "Content-Type: application/json" | jq '.'
+```
+
+#### Delete a file
+
+```bash
+curl -X DELETE http://${HOST_IP}:16010/v1/data/files/test2.docx -H "Content-Type: application/json" | jq '.'
+```
+
+#### Update a file
+
+```bash
+curl -X PATCH http://${HOST_IP}:16010/v1/data/files/test.pdf -H "Content-Type: application/json" -d @examples/test_data_file.json | jq '.'
+```