Add opensearch integration for OPEA

.github/workflows/docker/compose/dataprep-compose.yaml

@@ -67,3 +67,7 @@ services:
     build:
       dockerfile: comps/dataprep/elasticsearch/langchain/Dockerfile
     image: ${REGISTRY:-opea}/dataprep-elasticsearch:${TAG:-latest}
+  dataprep-opensearch:
+    build:
+      dockerfile: comps/dataprep/opensearch/langchain/Dockerfile
+    image: ${REGISTRY:-opea}/dataprep-opensearch:${TAG:-latest}

.github/workflows/docker/compose/retrievers-compose.yaml

@@ -51,3 +51,7 @@ services:
     build:
       dockerfile: comps/retrievers/elasticsearch/langchain/Dockerfile
     image: ${REGISTRY:-opea}/retriever-elasticsearch:${TAG:-latest}
+  retriever-opensearch:
+    build:
+      dockerfile: comps/retrievers/opensearch/langchain/Dockerfile
+    image: ${REGISTRY:-opea}/retriever-opensearch:${TAG:-latest}

comps/dataprep/opensearch/README.md

@@ -0,0 +1,253 @@
+# Dataprep Microservice with OpenSearch
+
+For dataprep microservice for text input, we provide here the `Langchain` framework.
+
+## 🚀1. Start Microservice with Python（Option 1）
+
+### 1.1 Install Requirements
+
+- option 1: Install Single-process version (for processing up to 10 files)
+
+```bash
+apt update
+apt install default-jre tesseract-ocr libtesseract-dev poppler-utils -y
+# for langchain
+cd langchain
+pip install -r requirements.txt
+```
+
+### 1.2 Start OpenSearch Stack Server
+
+Please refer to this [readme](../../vectorstores/opensearch/README.md).
+
+### 1.3 Setup Environment Variables
+
+```bash
+export your_ip=$(hostname -I | awk '{print $1}')
+export OPENSEARCH_URL="http://${your_ip}:9200"
+export INDEX_NAME=${your_index_name}
+export PYTHONPATH=${path_to_comps}
+```
+
+### 1.4 Start Embedding Service
+
+First, you need to start a TEI service.
+
+```bash
+your_port=6006
+model="BAAI/bge-base-en-v1.5"
+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-1.5 --model-id $model
+```
+
+Then you need to test your TEI service using the following commands:
+
+```bash
+curl localhost:$your_port/embed \
+    -X POST \
+    -d '{"inputs":"What is Deep Learning?"}' \
+    -H 'Content-Type: application/json'
+```
+
+After checking that it works, set up environment variables.
+
+```bash
+export TEI_ENDPOINT="http://localhost:$your_port"
+```
+
+### 1.4 Start Document Preparation Microservice for OpenSearch with Python Script
+
+Start document preparation microservice for OpenSearch with below command.
+
+- option 1: Start single-process version (for processing up to 10 files)
+
+```bash
+cd langchain
+python prepare_doc_opensearch.py
+```
+
+## 🚀2. Start Microservice with Docker (Option 2)
+
+### 2.1 Start OpenSearch Stack Server
+
+Please refer to this [readme](../../vectorstores/opensearch/README.md).
+
+### 2.2 Setup Environment Variables
+
+```bash
+export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
+export TEI_ENDPOINT="http://${your_ip}:6006"
+export OPENSEARCH_URL="http://${your_ip}:9200"
+export INDEX_NAME=${your_index_name}
+export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
+```
+
+### 2.3 Build Docker Image
+
+- Build docker image with langchain
+
+- option 1: Start single-process version (for processing up to 10 files)
+
+```bash
+cd ../../
+docker build -t opea/dataprep-opensearch:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/opensearch/langchain/Dockerfile .
+```
+
+### 2.4 Run Docker with CLI (Option A)
+
+- option 1: Start single-process version (for processing up to 10 files)
+
+```bash
+docker run -d --name="dataprep-opensearch-server" -p 6007:6007 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e OPENSEARCH_URL=$OPENSEARCH_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN opea/dataprep-opensearch:latest
+```
+
+### 2.5 Run with Docker Compose (Option B - deprecated, will move to genAIExample in future)
+
+```bash
+# for langchain
+cd comps/dataprep/opensearch/langchain
+# common command
+docker compose -f docker-compose-dataprep-opensearch.yaml up -d
+```
+
+## 🚀3. Status Microservice
+
+```bash
+docker container logs -f dataprep-opensearch-server
+```
+
+## 🚀4. Consume Microservice
+
+### 4.1 Consume Upload API
+
+Once document preparation microservice for OpenSearch is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database.
+
+Make sure the file path after `files=@` is correct.
+
+- Single file upload
+
+```bash
+curl -X POST \
+    -H "Content-Type: multipart/form-data" \
+    -F "files=@./file1.txt" \
+    http://localhost:6007/v1/dataprep
+```
+
+You can specify chunk_size and chunk_size by the following commands.
+
+```bash
+curl -X POST \
+    -H "Content-Type: multipart/form-data" \
+    -F "files=@./file1.txt" \
+    -F "chunk_size=1500" \
+    -F "chunk_overlap=100" \
+    http://localhost:6007/v1/dataprep
+```
+
+We support table extraction from pdf documents. You can specify process_table and table_strategy by the following commands. "table_strategy" refers to the strategies to understand tables for table retrieval. As the setting progresses from "fast" to "hq" to "llm," the focus shifts towards deeper table understanding at the expense of processing speed. The default strategy is "fast".
+
+Note: If you specify "table_strategy=llm", You should first start TGI Service, please refer to 1.2.1, 1.3.1 in https://github.com/opea-project/GenAIComps/tree/main/comps/llms/README.md, and then `export TGI_LLM_ENDPOINT="http://${your_ip}:8008"`.
+
+```bash
+curl -X POST \
+    -H "Content-Type: multipart/form-data" \
+    -F "files=@./your_file.pdf" \
+    -F "process_table=true" \
+    -F "table_strategy=hq" \
+    http://localhost:6007/v1/dataprep
+```
+
+- Multiple file upload
+
+```bash
+curl -X POST \
+    -H "Content-Type: multipart/form-data" \
+    -F "files=@./file1.txt" \
+    -F "files=@./file2.txt" \
+    -F "files=@./file3.txt" \
+    http://localhost:6007/v1/dataprep
+```
+
+- Links upload (not supported for llama_index now)
+
+```bash
+curl -X POST \
+    -F 'link_list=["https://www.ces.tech/"]' \
+    http://localhost:6007/v1/dataprep
+```
+
+or
+
+```python
+import requests
+import json
+
+proxies = {"http": ""}
+url = "http://localhost:6007/v1/dataprep"
+urls = [
+    "https://towardsdatascience.com/no-gpu-no-party-fine-tune-bert-for-sentiment-analysis-with-vertex-ai-custom-jobs-d8fc410e908b?source=rss----7f60cf5620c9---4"
+]
+payload = {"link_list": json.dumps(urls)}
+
+try:
+    resp = requests.post(url=url, data=payload, proxies=proxies)
+    print(resp.text)
+    resp.raise_for_status()  # Raise an exception for unsuccessful HTTP status codes
+    print("Request successful!")
+except requests.exceptions.RequestException as e:
+    print("An error occurred:", e)
+```
+
+### 4.2 Consume get_file API
+
+To get uploaded file structures, use the following command:
+
+```bash
+curl -X POST \
+    -H "Content-Type: application/json" \
+    http://localhost:6007/v1/dataprep/get_file
+```
+
+Then you will get the response JSON like this:
+
+```json
+[
+  {
+    "name": "uploaded_file_1.txt",
+    "id": "uploaded_file_1.txt",
+    "type": "File",
+    "parent": ""
+  },
+  {
+    "name": "uploaded_file_2.txt",
+    "id": "uploaded_file_2.txt",
+    "type": "File",
+    "parent": ""
+  }
+]
+```
+
+### 4.3 Consume delete_file API
+
+To delete uploaded file/link, use the following command.
+
+The `file_path` here should be the `id` get from `/v1/dataprep/get_file` API.
+
+```bash
+# delete link
+curl -X POST \
+    -H "Content-Type: application/json" \
+    -d '{"file_path": "https://www.ces.tech/.txt"}' \
+    http://localhost:6007/v1/dataprep/delete_file
+
+# delete file
+curl -X POST \
+    -H "Content-Type: application/json" \
+    -d '{"file_path": "uploaded_file_1.txt"}' \
+    http://localhost:6007/v1/dataprep/delete_file
+
+# delete all files and links
+curl -X POST \
+    -H "Content-Type: application/json" \
+    -d '{"file_path": "all"}' \
+    http://localhost:6007/v1/dataprep/delete_file
+```

comps/dataprep/opensearch/langchain/Dockerfile

@@ -0,0 +1,42 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+FROM python:3.11-slim
+
+ENV LANG=C.UTF-8
+
+ARG ARCH="cpu"
+
+RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
+    build-essential \
+    default-jre \
+    libgl1-mesa-glx \
+    libjemalloc-dev \
+    libreoffice \
+    poppler-utils \
+    tesseract-ocr
+
+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 setuptools && \
+    if [ ${ARCH} = "cpu" ]; then pip install --no-cache-dir torch torchvision --index-url https://download.pytorch.org/whl/cpu; fi && \
+    pip install --no-cache-dir -r /home/user/comps/dataprep/opensearch/langchain/requirements.txt
+
+ENV PYTHONPATH=$PYTHONPATH:/home/user
+
+USER root
+
+RUN mkdir -p /home/user/comps/dataprep/opensearch/langchain/uploaded_files && chown -R user /home/user/comps/dataprep/opensearch/langchain/uploaded_files
+
+USER user
+
+WORKDIR /home/user/comps/dataprep/opensearch/langchain
+
+ENTRYPOINT ["python", "prepare_doc_opensearch.py"]
+

comps/dataprep/opensearch/langchain/__init__.py

@@ -0,0 +1,2 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0

comps/dataprep/opensearch/langchain/config.py

@@ -0,0 +1,60 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+
+# Embedding model
+EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5")
+
+# OpenSearch Connection Information
+OPENSEARCH_HOST = os.getenv("OPENSEARCH_HOST", "localhost")
+OPENSEARCH_PORT = int(os.getenv("OPENSEARCH_PORT", 9200))
+OPENSEARCH_INITIAL_ADMIN_PASSWORD = os.getenv("OPENSEARCH_INITIAL_ADMIN_PASSWORD", "")
+
+
+def get_boolean_env_var(var_name, default_value=False):
+    """Retrieve the boolean value of an environment variable.
+
+    Args:
+    var_name (str): The name of the environment variable to retrieve.
+    default_value (bool): The default value to return if the variable
+    is not found.
+
+    Returns:
+    bool: The value of the environment variable, interpreted as a boolean.
+    """
+    true_values = {"true", "1", "t", "y", "yes"}
+    false_values = {"false", "0", "f", "n", "no"}
+
+    # Retrieve the environment variable's value
+    value = os.getenv(var_name, "").lower()
+
+    # Decide the boolean value based on the content of the string
+    if value in true_values:
+        return True
+    elif value in false_values:
+        return False
+    else:
+        return default_value
+
+
+def format_opensearch_conn_from_env():
+    opensearch_url = os.getenv("OPENSEARCH_URL", None)
+    if opensearch_url:
+        return opensearch_url
+    else:
+        using_ssl = get_boolean_env_var("OPENSEARCH_SSL", False)
+        start = "https://" if using_ssl else "http://"
+
+        return start + f"{OPENSEARCH_HOST}:{OPENSEARCH_PORT}"
+
+
+OPENSEARCH_URL = format_opensearch_conn_from_env()
+
+# Vector Index Configuration
+INDEX_NAME = os.getenv("INDEX_NAME", "rag-opensearch")
+KEY_INDEX_NAME = os.getenv("KEY_INDEX_NAME", "file-keys")
+
+TIMEOUT_SECONDS = int(os.getenv("TIMEOUT_SECONDS", 600))
+
+SEARCH_BATCH_SIZE = int(os.getenv("SEARCH_BATCH_SIZE", 10))