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Add new DocIndexRetriever example (#405)
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* Add DocIndexRetriever example

Signed-off-by: Chendi.Xue <chendi.xue@intel.com>


---------

Signed-off-by: Chendi.Xue <chendi.xue@intel.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: XuhuiRen <44249229+XuhuiRen@users.noreply.github.com>
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8 changes: 8 additions & 0 deletions DocIndexRetriever/README.md
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# DocRetriever Application

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.

## We provided DocRetriever with different deployment infra

- [docker xeon version](docker/xeon/) => minimum endpoints, easy to setup
- [docker gaudi version](docker/gaudi/) => with extra tei_gaudi endpoint, faster
30 changes: 30 additions & 0 deletions DocIndexRetriever/docker/Dockerfile
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# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

FROM python:3.11-slim

COPY GenAIComps /home/user/GenAIComps

RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
libgl1-mesa-glx \
libjemalloc-dev \
vim \
git

RUN useradd -m -s /bin/bash user && \
mkdir -p /home/user && \
chown -R user /home/user/

WORKDIR /home/user/GenAIComps
RUN pip install --no-cache-dir --upgrade pip && \
pip install --no-cache-dir -r /home/user/GenAIComps/requirements.txt

COPY GenAIExamples/DocIndexRetriever/docker/retrieval_tool.py /home/user/retrieval_tool.py

ENV PYTHONPATH=$PYTHONPATH:/home/user/GenAIComps

USER user

WORKDIR /home/user

ENTRYPOINT ["python", "retrieval_tool.py"]
126 changes: 126 additions & 0 deletions DocIndexRetriever/docker/README.md
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# DocRetriever Application

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. (This step will not needed after docker image released)

- Embedding TEI Image

```bash
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/langchain/docker/Dockerfile .
```

- Retriever Vector store Image

```bash
docker build -t opea/retriever-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/langchain/redis/docker/Dockerfile .
```

- Rerank TEI Image

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

- Dataprep Image

```bash
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/docker/Dockerfile .
```

### 2. Build Images for MegaService

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

### 3. Start all the services Docker Containers

```bash
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}:8090"
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"
export llm_hardware='xeon' #xeon, xpu, gaudi
cd GenAIExamples/DocIndexRetriever/docker/${llm_hardware}/
docker compose -f docker-compose.yaml up -d
```

### 3. Validation

Add Knowledge Base via HTTP Links:

```bash
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

```bash
curl http://${host_ip}:8889/v1/retrievaltool -X POST -H "Content-Type: application/json" -d '{
"text": "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?"}
```

### 4. Trouble shooting

1. check all containers are alive

```bash
# 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
```
125 changes: 125 additions & 0 deletions DocIndexRetriever/docker/gaudi/docker_compose.yaml
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# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

version: "3.8"

services:
redis-vector-db:
image: redis/redis-stack:7.2.0-v9
container_name: redis-vector-db
ports:
- "16379:6379"
- "8001:8001"
dataprep-redis-service:
image: opea/dataprep-on-ray-redis:latest
container_name: dataprep-redis-server
depends_on:
- redis-vector-db
ports:
- "6007:6007"
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
REDIS_URL: ${REDIS_URL}
INDEX_NAME: ${INDEX_NAME}
tei-embedding-service:
image: ghcr.io/huggingface/tei-gaudi:latest
container_name: tei-embedding-gaudi-server
ports:
- "8090:80"
volumes:
- "./data:/data"
runtime: habana
cap_add:
- SYS_NICE
ipc: host
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
HABANA_VISIBLE_DEVICES: all
OMPI_MCA_btl_vader_single_copy_mechanism: none
MAX_WARMUP_SEQUENCE_LENGTH: 512
command: --model-id ${EMBEDDING_MODEL_ID}
embedding:
image: opea/embedding-tei:latest
container_name: embedding-tei-server
ports:
- "6000:6000"
ipc: host
depends_on:
- tei-embedding-service
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
TEI_EMBEDDING_ENDPOINT: ${TEI_EMBEDDING_ENDPOINT}
LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
LANGCHAIN_TRACING_V2: ${LANGCHAIN_TRACING_V2}
LANGCHAIN_PROJECT: "opea-embedding-service"
restart: unless-stopped
retriever:
image: opea/retriever-redis:latest
container_name: retriever-redis-server
depends_on:
- redis-vector-db
ports:
- "7000:7000"
ipc: host
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
REDIS_URL: ${REDIS_URL}
INDEX_NAME: ${INDEX_NAME}
LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
LANGCHAIN_TRACING_V2: ${LANGCHAIN_TRACING_V2}
LANGCHAIN_PROJECT: "opea-retriever-service"
restart: unless-stopped
reranking:
image: opea/reranking-tei:latest
container_name: reranking-tei-server
ports:
- "18000:8000"
ipc: host
entrypoint: python local_reranking.py
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
TEI_RERANKING_ENDPOINT: ${TEI_RERANKING_ENDPOINT}
HUGGINGFACEHUB_API_TOKEN: ${HUGGINGFACEHUB_API_TOKEN}
HF_HUB_DISABLE_PROGRESS_BARS: 1
HF_HUB_ENABLE_HF_TRANSFER: 0
LANGCHAIN_API_KEY: ${LANGCHAIN_API_KEY}
LANGCHAIN_TRACING_V2: ${LANGCHAIN_TRACING_V2}
LANGCHAIN_PROJECT: "opea-reranking-service"
restart: unless-stopped
doc-index-retriever-server:
image: opea/doc-index-retriever:latest
container_name: doc-index-retriever-server
depends_on:
- redis-vector-db
- tei-embedding-service
- embedding
- retriever
- reranking
ports:
- "8889:8889"
environment:
- no_proxy=${no_proxy}
- https_proxy=${https_proxy}
- http_proxy=${http_proxy}
- MEGA_SERVICE_HOST_IP=${MEGA_SERVICE_HOST_IP}
- EMBEDDING_SERVICE_HOST_IP=${EMBEDDING_SERVICE_HOST_IP}
- RETRIEVER_SERVICE_HOST_IP=${RETRIEVER_SERVICE_HOST_IP}
- RERANK_SERVICE_HOST_IP=${RERANK_SERVICE_HOST_IP}
- LLM_SERVICE_HOST_IP=${LLM_SERVICE_HOST_IP}
ipc: host
restart: always

networks:
default:
driver: bridge
59 changes: 59 additions & 0 deletions DocIndexRetriever/docker/retrieval_tool.py
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# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

import asyncio
import os

from comps import MicroService, RetrievalToolGateway, ServiceOrchestrator, ServiceType

MEGA_SERVICE_HOST_IP = os.getenv("MEGA_SERVICE_HOST_IP", "0.0.0.0")
MEGA_SERVICE_PORT = os.getenv("MEGA_SERVICE_PORT", 8889)
EMBEDDING_SERVICE_HOST_IP = os.getenv("EMBEDDING_SERVICE_HOST_IP", "0.0.0.0")
EMBEDDING_SERVICE_PORT = os.getenv("EMBEDDING_SERVICE_PORT", 6000)
RETRIEVER_SERVICE_HOST_IP = os.getenv("RETRIEVER_SERVICE_HOST_IP", "0.0.0.0")
RETRIEVER_SERVICE_PORT = os.getenv("RETRIEVER_SERVICE_PORT", 7000)
RERANK_SERVICE_HOST_IP = os.getenv("RERANK_SERVICE_HOST_IP", "0.0.0.0")
RERANK_SERVICE_PORT = os.getenv("RERANK_SERVICE_PORT", 8000)


class RetrievalToolService:
def __init__(self, host="0.0.0.0", port=8000):
self.host = host
self.port = port
self.megaservice = ServiceOrchestrator()

def add_remote_service(self):
embedding = MicroService(
name="embedding",
host=EMBEDDING_SERVICE_HOST_IP,
port=EMBEDDING_SERVICE_PORT,
endpoint="/v1/embeddings",
use_remote_service=True,
service_type=ServiceType.EMBEDDING,
)
retriever = MicroService(
name="retriever",
host=RETRIEVER_SERVICE_HOST_IP,
port=RETRIEVER_SERVICE_PORT,
endpoint="/v1/retrieval",
use_remote_service=True,
service_type=ServiceType.RETRIEVER,
)
rerank = MicroService(
name="rerank",
host=RERANK_SERVICE_HOST_IP,
port=RERANK_SERVICE_PORT,
endpoint="/v1/reranking",
use_remote_service=True,
service_type=ServiceType.RERANK,
)

self.megaservice.add(embedding).add(retriever).add(rerank)
self.megaservice.flow_to(embedding, retriever)
self.megaservice.flow_to(retriever, rerank)
self.gateway = RetrievalToolGateway(megaservice=self.megaservice, host="0.0.0.0", port=self.port)


if __name__ == "__main__":
chatqna = RetrievalToolService(host=MEGA_SERVICE_HOST_IP, port=MEGA_SERVICE_PORT)
chatqna.add_remote_service()
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