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Community Blog OpenSearch: A One Stop Solution to Easily Integrate LLM Generative AI in Your Application

OpenSearch: A One Stop Solution to Easily Integrate LLM Generative AI in Your Application

To create intelligent search, OpenSearch is a one-stop Solution as a Service (SaaS) technology that is applicable to industry-specific search scenario.

By Aaron Handoko, Solution Architect Alibaba Cloud

What is OpenSearch?

To create intelligent search, OpenSearch is a one-stop Solution as a Service (SaaS) technology that is applicable to industry-specific search scenarios and provides a dedicated conversational search service for enterprises. There are 5 edition of Open Search and we are going to use LLM-Based Conversational Search Edition in this blog.

image

Source: https://www.alibabacloud.com/help/en/open-search/llm-intelligent-q-a-version/introduction-to-llm-intelligent-q-a-edition?spm=a2c63.p38356.0.0.7bfa23c4qd7cAa

The diagram above is the architecture of the LLM Based Open Search including how data is being retrieved and processed as well as how the vector search algorithm is used to get the most relevant content to the user’s question.

LLM-Based Conversational Search Edition can automatically generate conversational search results in various formats such as texts, reference images, and reference links based on business data. The conversational search service is intelligent and high-quality.

Advantages of LLM-Based Conversational OpenSearch?

  • Dedicated conversational search service for enterprises
    It generates conversational search results solely based on business data. This makes the search results secure, stable, high-quality, and reliable as it produces little to no hallucinating answer.
  • Deployment-free, fully managed, and O&M-free
    A fully managed service without the need for deployment and O&M costs.
  • Multimodal search
    LLM-Based Conversational Search Edition provides search results in multiple formats such as texts, links, and images. This meets personalized conversational search requirements.
  • Ease of use
    A fully integrated process allows you to build an end-to-end enterprise-specific conversational search service by importing only business data.
  • Abundant amount of features
    The LLM-Based Conversational Search has numerous parameters that can be set to meet your business need.

Step by Step Integrating Open Search LLM in your Application

  • Purchase Open Search by accessing here.
    Edition: LLM-Based Conversational Search Edition

Instance Name:
Storage Capacity: The amount of GB needed for dataset

  • After the instance is purchased. Go into the instance by clicking the Manage button. Make sure that the LLM-Based Conversational Search Edition is selected.

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In this blog we will show how to manage dataset for LLM Conversational Search using
1) OpenSearch Console
2) SDK Python

1. OpenSearch Console

image
Figure 1. The Console of OpenSearch

A. Choose the Data Configuration on the left-hand side. You can choose to import data using API or File. Choose Import File then choose between Structured Data (data example can be found here) and Unstructured Data (pdf, docx, doc, html). Upload the data that you want to set as the dataset.

image

B. After the dataset is uploaded, the data will be shown in the console. Check the content of the data by clicking View Content.

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C. We can also set up manual intervention if we want the LLM to provide specific answer to a question. Click Create. The picture below shows the example of question and answer.

image

D. Q&A Test. This is the platform to do testing for the Conversational Search Edition by typing the questions on the box provided. On the right-hand side there are numerous parameters that you can choose.

image

Q&A Parameters information can be found here.

Parameters Description
Model The LLM model that you can use. In Singapore region you can choose openbuddy_llama2_13b and opensearch-falcon-7b.
Multi-round Conversations (session) Conversation that understands multiple interaction with the customer. The number of questions needed for the model to make a decision.

Extended Parameters information can be found here.
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2. OpenSearch SDK using Python

In this blog we will integrate LLM using Python SDK. For Java or PHP language can be found here. [Java, PHP]

a) Clone the github repository containing helper code and simple application code

git clone https://github.com/aaronhandoko01/OpenSearchLLM.git

b) Install the necessary libraries

pip install alibabacloud_tea_util
pip install alibabacloud_opensearch_util
pip install alibabacloud_credentials

c) Create environmental variable for the Alibaba Cloud Access Key and Secret.
i) For Mac or Ubuntu users we can run the following code

export ALIBABA_CLOUD_ACCESS_KEY_ID=<access_key_id> 
export ALIBABA_CLOUD_ACCESS_KEY_SECRET=<access_key_secret>

ii) For Windows user we can do the following

1) Create an environment variable file, add the ALIBABA_CLOUD_ACCESS_KEY_ID and ALIBABA_CLOUD_ACCESS_KEY_SECRET environment variables to the file, and then set the environment variables to your AccessKey ID and AccessKey secret.

2) Restart Windows for the AccessKey pair to take effect.

d) BaseRequest.py is a helper code to configure the SDK call. The code below can be found in the github or here.

import time
from typing import Dict, Any

from Tea.core import TeaCore
from Tea.exceptions import TeaException, UnretryableException
from Tea.model import TeaModel
from Tea.request import TeaRequest
from alibabacloud_credentials import models as credential_models
from alibabacloud_credentials.client import Client as CredentialClient
from alibabacloud_opensearch_util.opensearch_util import OpensearchUtil
from alibabacloud_tea_util import models as util_models
from alibabacloud_tea_util.client import Client as UtilClient

class Config(TeaModel):
    """
    Config
    The environment-related configurations.
    """
    def __init__(
        self,
        endpoint: str = None,
        protocol: str = None,
        type: str = None,
        security_token: str = None,
        access_key_id: str = None,
        access_key_secret: str = None,
        user_agent: str = ""
        ):

        self.endpoint = endpoint
        self.protocol = protocol
        self.type = type
        self.security_token = security_token
        self.access_key_id = access_key_id
        self.access_key_secret = access_key_secret
        self.user_agent = user_agent

class Client:
    """
    OpensearchClient
    The OpenSearch client that is used to specify request parameters and send the request.
    """
    _endpoint: str = None
    _protocol: str = None
    _user_agent: str = None
    _credential: CredentialClient = None

    def __init__(self, config: Config):
        if UtilClient.is_unset(config):
            raise TeaException(
                {
                    'name': 'ParameterMissing',
                    'message': "'config' can not be unset"
                }
            )
        if UtilClient.empty(config.type):
            config.type = 'access_key'

        credential_config = credential_models.Config(
            access_key_id=config.access_key_id,
            type=config.type,
            access_key_secret=config.access_key_secret,
            security_token=config.security_token
        )

        self._credential = CredentialClient(credential_config)
        self._endpoint = config.endpoint
        self._protocol = config.protocol
        self._user_agent = config.user_agent

    def _request(self, method: str, pathname: str, query: Dict[str, Any], headers: Dict[str, str], body: Any, runtime: util_models.RuntimeOptions,) -> Dict[str, Any]:
        """
        Process the request.
        :param request: TeaRequest
        :param runtime: util_models.RuntimeOptions
        :return: Dict[str, Any]
        """
        runtime.validate()
        _runtime = {
            'timeouted': 'retry',
            'readTimeout': runtime.read_timeout,
            'connectTimeout': runtime.connect_timeout,
            'httpProxy': runtime.http_proxy,
            'httpsProxy': runtime.https_proxy,
            'noProxy': runtime.no_proxy,
            'maxIdleConns': runtime.max_idle_conns,
            'retry': {
                'retryable': runtime.autoretry,
                'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)
            },
            'backoff': {
                'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),
                'period': UtilClient.default_number(runtime.backoff_period, 1)
            },
            'ignoreSSL': runtime.ignore_ssl
        }
        _last_request = None
        _last_exception = None
        _now = time.time()
        _retry_times = 0

        while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):
            if _retry_times > 0:
                _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)
                if _backoff_time > 0:
                    TeaCore.sleep(_backoff_time)
                    _retry_times = _retry_times + 1
            try:
                _request = TeaRequest()
                accesskey_id = self._credential.get_access_key_id()
                access_key_secret = self._credential.get_access_key_secret()
                security_token = self._credential.get_security_token()
                _request.protocol = UtilClient.default_string(self._protocol, 'HTTP')
                _request.method = method
                _request.pathname = pathname
                _request.headers = TeaCore.merge({
                    'user-agent': UtilClient.get_user_agent(self._user_agent),
                    'Content-Type': 'application/json',
                    'Date': OpensearchUtil.get_date(),
                    'host': UtilClient.default_string(self._endpoint, f'opensearch-cn-hangzhou.aliyuncs.com'),
                    'X-Opensearch-Nonce': UtilClient.get_nonce()
                }, headers)

                if not UtilClient.is_unset(query):
                    _request.query = UtilClient.stringify_map_value(query)
                if not UtilClient.is_unset(body):
                    req_body = UtilClient.to_jsonstring(body)
                    _request.headers['Content-MD5'] = OpensearchUtil.get_content_md5(req_body)
                    _request.body = req_body
                if not UtilClient.is_unset(security_token):
                    _request.headers["X-Opensearch-Security-Token"] = security_token

                _request.headers['Authorization'] = OpensearchUtil.get_signature(_request, accesskey_id, access_key_secret)
                _last_request = _request
                _response = TeaCore.do_action(_request, _runtime)
                obj_str = UtilClient.read_as_string(_response.body)

                if UtilClient.is_4xx(_response.status_code) or UtilClient.is_5xx(_response.status_code):
                    raise TeaException({
                        'message': _response.status_message,
                        'data': obj_str,
                        'code': _response.status_code
                    })

                obj = UtilClient.parse_json(obj_str)
                res = UtilClient.assert_as_map(obj)

                return {
                    'body': res,
                    'headers': _response.headers
                }
            except TeaException as e:
                if TeaCore.is_retryable(e):
                    _last_exception = e
                    continue
                raise e
        raise UnretryableException(_last_request, _last_exception)
  1. Create simple application in Python to push documents and do LLM Q&A function
import time, os
from typing import Dict, Any

from Tea.exceptions import TeaException
from Tea.request import TeaRequest
from alibabacloud_tea_util import models as util_models
# from opensearch.V3_cases.doc_search.BaseRequest import Config, Client
from BaseRequest import Config, Client

class LLMSearch:
    def __init__(self, config: Config):
        self.Clients = Client(config=config)
        self.runtime = util_models.RuntimeOptions(
            connect_timeout=10000,
            read_timeout=10000,
            autoretry=False,
            ignore_ssl=False,
            max_idle_conns=50,
            max_attempts=3
        )
        self.header = {}

    def searchDoc(self, app_name: str,body:Dict, query_params: dict={}) -> Dict[str, Any]:
        try:
            response = self.Clients._request(method="POST", pathname=f'/v3/openapi/apps/{app_name}/actions/knowledge-search',
                                             query=query_params, headers=self.header, body=body, runtime=self.runtime)
            return response
        except TeaException as e:
            print(e)

class LLMDocumentPush:
    def __init__(self, config: Config):
        self.Clients = Client(config=config)
        self.runtime = util_models.RuntimeOptions(
            connect_timeout=10000,
            read_timeout=10000,
            autoretry=False,
            ignore_ssl=False,
            max_idle_conns=50,
            max_attempts=3
        )
        self.header = {}

    def docBulk(self, app_name: str,doc_content: list) -> Dict[str, Any]:
        try:
            response = self.Clients._request(method="POST",
                                             pathname=f'/v3/openapi/apps/{app_name}/actions/knowledge-bulk',
                                             query={}, headers=self.header,
                                             body=doc_content, runtime=self.runtime)
            return response
        except Exception as e:
            print(e)

def search_llm(app_name, ops, question):

    # --------------- Search for documents ---------------

    docQuery = {
            "question": {
                "text": question, 
                "type": "TEXT",
                "session": 10,
                # "content": "Legal Agreement Form.pdf"
            },
            "options": {
                "retrieve": {
                    "doc": {
                        "disable": False, # Specifies whether to disable the document retrieval feature. Default value: false. 
                        "filter": 'category="Hotel"', # Filters documents based on the specified category during document retrieval. By default, this parameter is left empty.
                        "sf": 1.3,    # The threshold of vector relevance for document retrieval. Default value: 1.3. The greater the value is, the less relevant the retrieved documents are.
                        "top_n": 5,    # The number of documents to be retrieved. Default value: 5. Valid values: (0,50].
                        "formula" : "", # By default, documents are retrieved based on vector similarity.
                        # "rerank_size" : 5, # The number of documents to be fine sorted. By default, you do not need to specify this parameter, and the system determines the number of documents to be fine sorted.
                        "operator":"AND"   # The operator between text tokens. In this example, the OR operator is used between text tokens when text documents are retrieved. Default value: AND.                                  
                    },
                    "entry": {
                        "disable": False, # Specifies whether to disable the intervention data retrieval feature. Default value: false. 
                        "sf": 0.3 # The vector relevance for intervention data retrieval. Default value: 0.3.
                    },
                },
                "chat": {
                    "stream" : False, # Specifies whether to enable HTTP chunked transfer encoding. Default value: false. 
                    "disable" : False, # specifies whether to disable the chat model. Default value: false. 
                    "model" : "opensearch-llama2-13b", # The LLM model. Valid values: Qwen and opensearch-llama2-13b.
                    "prompt_config": {
                        "attitude": "normal",
                        "rule": "stepbystep",
                        "noanswer": "sorry",
                        "language": "English",
                        "role": True,
                        "role_name": "AI Assistant"
                        # "out_format": "text"
                    }
                },
            }}

    res1 = ops.searchDoc(app_name=app_name, body=docQuery)
    # print("ANSWER ", res1["body"]["result"]["data"][0]["answer"])

    return res1["body"]["result"]["data"][0]["answer"]
    # print(res1)

def push_doc(app_name, ops):
    document = [
        {
            "fields": {
                "id": "1",
                "title": "Benefits",
                "url": "https://help.aliyun.com/document_detail/464900.html",
                "content": "Industry Algorithm Edition: Intelligence: Industry Algorithm Edition provides rich built-in and customized algorithm models and introduces industry retrieval and sorting algorithms based on the search needs of different industries.",
                "category": "opensearch",
                "timestamp": 1691722088645,
                "score": 0.8821945219723084
            },
            "cmd": "ADD"
        },
        {
            "fields": {
                "id": "2",
                "title": "Scenarios",
                "url": "https://help.aliyun.com/document_detail/464901.html",
                "content": "Industry Algorithm Edition: Features: provides industry built-in capabilities such as semantic understanding and machine learning-based algorithms, and supports lightweight custom models and search guidance. This helps you build intelligent search services in a quick manner",
                "category": "opensearch",
                "timestamp": 1691722088646,
                "score": 0.8993507402088953
            },
            "cmd": "ADD"
        }
    ]

    # Delete a record.
    deletedocument = {"cmd": "DELETE", "fields": {"id": 2}}
    documents = document
    res5 = ops.docBulk(app_name=app_name, doc_content=documents)
    return res5
    


if __name__ == "__main__":
    # Specify the endpoint of the OpenSearch API. The value does not contain the http:// prefix.
    endpoint = "opensearch-ap-southeast-1.aliyuncs.com"

    # Specify the request protocol. Valid values: HTTPS and HTTP.
    endpoint_protocol = "HTTP"

    # Specify your AccessKey pair.
    # Obtain the AccessKey ID and AccessKey secret from environment variables. 
    # You must configure environment variables before you run this code. For more information, see the "Configure environment variables" section of this topic.
    access_key_id = os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_ID")
    access_key_secret = os.environ.get("ALIBABA_CLOUD_ACCESS_KEY_SECRET")

    # Specify the authentication method. Default value: access_key. A value of sts indicates authentication based on Resource Access Management (RAM) and Security Token Service (STS).
    # Valid values: sts and access_key.
    auth_type = "access_key"

    # If you use authentication based on RAM and STS, you must specify the security_token parameter. You can call the AssumeRole operation of Alibaba Cloud RAM to obtain an STS token.
    security_token =  "<security_token>"

    # Specify common request parameters.
    # The type and security_token parameters are required only if you use the SDK as a RAM user.
    Configs = Config(endpoint=endpoint, access_key_id=access_key_id, access_key_secret=access_key_secret,
                     security_token=security_token, type=auth_type, protocol=endpoint_protocol)

    # Create an OpenSearch instance.
    app_name = "demo_llm"

    # print(res1)
    while True:
        print("\t\tWELCOME TO CHAT")
        print("1. Q&A Feature: ")
        print("2. Push Document: ")

        choice = int(input("Option: "))
        if choice ==1:
            ops = LLMSearch(Configs)
            print("What can I help?")
            while True:
                question = input("\n")

                if not question.lower() == 'exit':
                    print("\n\nResponse: ", search_llm(app_name, ops, question))
                else:
                    break

        elif choice == 2:
            ops = LLMDocumentPush(Configs)
            print("STATUS: ", push_doc(app_name, ops))
        else:
            break

To push the document into OpenSearch we create LLMDocumentPush class and call the function push_doc. To create Q&A function we create LLMSearch class and use search_llm function. The parameters for the LLM Q&A can be modified in the search_llm function.

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Here is the example of the simple terminal application using Bahasa Indonesia Language:

image

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