Add docker & readme for Embedding Microservice (opea-project#22)

* add docker & readme for embedding service

Signed-off-by: letonghan <[email protected]>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Signed-off-by: letonghan <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

comps/embeddings/README.md

@@ -0,0 +1,106 @@
+# Embeddings Microservice
+
+The Embedding Microservice is designed to efficiently convert textual strings into vectorized embeddings, facilitating seamless integration into various machine learning and data processing workflows. This service utilizes advanced algorithms to generate high-quality embeddings that capture the semantic essence of the input text, making it ideal for applications in natural language processing, information retrieval, and similar fields.
+
+Key Features:
+
+**High Performance**: Optimized for quick and reliable conversion of textual data into vector embeddings.
+
+**Scalability**: Built to handle high volumes of requests simultaneously, ensuring robust performance even under heavy loads.
+
+**Ease of Integration**: Provides a simple and intuitive API, allowing for straightforward integration into existing systems and workflows.
+
+**Customizable**: Supports configuration and customization to meet specific use case requirements, including different embedding models and preprocessing techniques.
+
+Users are albe to configure and build embedding-related services according to their actual needs.
+
+# 🚀Start Microservice with Python
+
+Currently, we provide two ways to implement the embedding service:
+
+1. Build the embedding model **_locally_** from the server, which is faster, but takes up memory on the local server.
+
+2. Build it based on the **_TEI endpoint_**, which provides more flexibility, but may bring some network latency.
+
+For both of the implementations, you need to install requirements first.
+
+## Install Requirements
+
+```bash
+pip install -r langchain/requirements.txt
+```
+
+## Start Embedding Service with Local Model
+
+```bash
+python local_embedding.py
+```
+
+## Start Embedding Service with TEI
+
+First, you need to start a TEI service.
+
+```bash
+your_port=8090
+model="BAAI/bge-large-en-v1.5"
+revision="refs/pr/5"
+docker run -p $your_port:80 -v ./data:/data --name tei_server -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.2 --model-id $model --revision $revision
+```
+
+Then you need to test your TEI service using the following commands:
+
+```bash
+curl localhost:$your_port/embed \
+    -X POST \
+    -d '{"inputs":"What is Deep Learning?"}' \
+    -H 'Content-Type: application/json'
+```
+
+Start the embedding service with the TEI_ENDPOINT.
+
+```bash
+cd langchain
+export TEI_ENDPOINT="http://localhost:$yourport"
+python embedding_tei_gaudi.py
+```
+
+# 🚀Start Microservice with Docker
+
+## Build Docker Image
+
+```bash
+cd ../../
+docker build -t genaicomps/microservice/embedding:0.0.1 --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/docker/Dockerfile .
+```
+
+## Run Docker with CLI
+
+```bash
+docker run -d --name="embedding_server" -p 6000:6000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TEI_ENDPOINT=$TEI_ENDPOINT  genaicomps/microservice/embedding:0.0.1
+```
+
+## Run Docker with Docker Compose
+
+```bash
+cd docker
+docker compose -f docker_compose.yaml up -d
+```
+
+# 🚀Consume Embedding Service
+
+## Check Service Status
+
+```bash
+curl http://localhost:6000/v1/health_check\
+  -X GET \
+  -H 'Content-Type: application/json'
+```
+
+## Consume Embedding Service
+
+```bash
+curl http://localhost:6000/v1/embeddings\
+  -X POST \
+  -d '{"text":"Hello, world!"}' \
+  -H 'Content-Type: application/json'
+```

comps/embeddings/docker/Dockerfile

@@ -0,0 +1,38 @@
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+FROM langchain/langchain:latest
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+    libgl1-mesa-glx \
+    libjemalloc-dev \
+    vim
+
+RUN useradd -m -s /bin/bash user && \
+    mkdir -p /home/user && \
+    chown -R user /home/user/
+
+USER user
+
+COPY comps /home/user/comps
+
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r /home/user/comps/embeddings/langchain/requirements.txt
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user
+
+WORKDIR /home/user/comps/embeddings/langchain
+
+ENTRYPOINT ["python", "embedding_tei_gaudi.py"]
+

comps/embeddings/docker/docker_compose_embedding.yaml

@@ -0,0 +1,32 @@
+# Copyright (c) 2024 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+version: "3.8"
+
+services:
+  embedding:
+    image: genaicomps/microservice/embedding:0.0.1
+    container_name: embedding_server
+    ports:
+      - "6000:6000"
+    ipc: host
+    environment:
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      TEI_ENDPOINT: ${TEI_ENDPOINT}
+    restart: unless-stopped
+
+networks:
+  default:
+    driver: bridge

comps/embeddings/langchain/embedding_tei_gaudi.py

@@ -22,13 +22,14 @@
 @register_microservice(
     name="opea_service@embedding_tgi_gaudi",
     expose_endpoint="/v1/embeddings",
+    host="0.0.0.0",
     port=6000,
     input_datatype=TextDoc,
     output_datatype=EmbedDoc1024,
 )
 def embedding(input: TextDoc) -> TextDoc:
     embed_vector = embeddings.embed_query(input.text)
-    res = EmbedDoc1024(query=input.text, embedding=embed_vector)
+    res = EmbedDoc1024(text=input.text, embedding=embed_vector)
     return res
 
 

comps/embeddings/langchain/requirements.txt

@@ -1,2 +1,4 @@
+docarray[full]
+fastapi
 langchain
 sentence_transformers

comps/proto/docarray.py

@@ -31,7 +31,12 @@ class Base64ByteStrDoc(BaseDoc):
 
 class EmbedDoc768(BaseDoc):
     text: str
-    embedding: conlist(float, min_items=768, max_items=768)
+    embedding: conlist(float, min_length=768, max_length=768)
+
+
+class EmbedDoc1024(BaseDoc):
+    text: str
+    embedding: conlist(float, min_length=1024, max_length=1024)
 
 
 class Audio2TextDoc(AudioDoc):
@@ -49,11 +54,6 @@ class Audio2TextDoc(AudioDoc):
     )
 
 
-class EmbedDoc1024(BaseDoc):
-    text: str
-    embedding: conlist(float, min_items=1024, max_items=1024)
-
-
 class SearchedDoc(BaseDoc):
     retrieved_docs: DocList[TextDoc]
     initial_query: str